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Sample records for brain tumor segmentation

  1. Automated segmentation of MR images of brain tumors.

    PubMed

    Kaus, M R; Warfield, S K; Nabavi, A; Black, P M; Jolesz, F A; Kikinis, R

    2001-02-01

    An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas. PMID:11161183

  2. Multifractal texture estimation for detection and segmentation of brain tumors.

    PubMed

    Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

    2013-11-01

    A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

  3. Multiclass feature selection for improved pediatric brain tumor segmentation

    NASA Astrophysics Data System (ADS)

    Ahmed, Shaheen; Iftekharuddin, Khan M.

    2012-03-01

    In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

  4. Research of the multimodal brain-tumor segmentation algorithm

    NASA Astrophysics Data System (ADS)

    Lu, Yisu; Chen, Wufan

    2015-12-01

    It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.

  5. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

    PubMed

    Zhao, Liya; Jia, Kebin

    2016-01-01

    Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness. PMID:27069501

  6. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

    PubMed Central

    Zhao, Liya; Jia, Kebin

    2016-01-01

    Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness. PMID:27069501

  7. Brain tumor segmentation in MRI based on fuzzy aggregators

    NASA Astrophysics Data System (ADS)

    Zhu, Yan; Liao, Qingmin; Dou, Weibei; Ruan, Su

    2005-07-01

    Magnetic Resonance Image (MRI) is widely used in radiology diagnosis, especially in pathology detection in human brain. Most of the methods now applied to automatically segment brain tumors rely on T1-weighted sequences exclusively despite the fact that the imaging agent is multi-spectral. The work focuses on the integration or fusion of information provided by each sequence, i.e. T1, T2 and PD. Based on the fuzzy aggregators proposed in fuzzy theory, a system integrating all these information is established. The paper discusses some famous operators, their properties and application in tumor segmentation. In particular, Davies-Bouldin index is used to determine the parameters of the parametric operations. The result shows the importance of data fusion in segmentation process, discovers that T-norms are less robust to noise compared with mean operators. Meanwhile, weights allocated illustrate the order of importance of each spectrum in pathology detection, and are in agreement with their characteristic.

  8. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    PubMed Central

    Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B.; Ayache, Nicholas; Buendia, Patricia; Collins, D. Louis; Cordier, Nicolas; Corso, Jason J.; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R.; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M.; Jena, Raj; John, Nigel M.; Konukoglu, Ender; Lashkari, Danial; Mariz, José António; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J.; Raviv, Tammy Riklin; Reza, Syed M. S.; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A.; Sousa, Nuno; Subbanna, Nagesh K.; Szekely, Gabor; Taylor, Thomas J.; Thomas, Owen M.; Tustison, Nicholas J.; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2016-01-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource. PMID:25494501

  9. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

    PubMed

    Menze, Bjoern H; Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B; Ayache, Nicholas; Buendia, Patricia; Collins, D Louis; Cordier, Nicolas; Corso, Jason J; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M; Jena, Raj; John, Nigel M; Konukoglu, Ender; Lashkari, Danial; Mariz, José Antonió; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J; Raviv, Tammy Riklin; Reza, Syed M S; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A; Sousa, Nuno; Subbanna, Nagesh K; Szekely, Gabor; Taylor, Thomas J; Thomas, Owen M; Tustison, Nicholas J; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2015-10-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

  10. Confidence-based ensemble for GBM brain tumor segmentation

    NASA Astrophysics Data System (ADS)

    Huo, Jing; van Rikxoort, Eva M.; Okada, Kazunori; Kim, Hyun J.; Pope, Whitney; Goldin, Jonathan; Brown, Matthew

    2011-03-01

    It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.

  11. Segmenting nonenhancing brain tumors from normal tissues in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Fletcher-Heath, Lynn M.; Hall, Lawrence O.; Goldgof, Dmitry B.

    1998-06-01

    Tumor segmentation from magnetic resonance (MR) images aids in tumor treatment by tracking the progress of tumor growth and/or shrinkage. In this paper we present an automatic segmentation method which separates non-enhancing brain tumors from healthy tissues in MR images. The MR feature images used for the segmentation consist of three weighted images (T1, T2 and proton density) for each axial slice through the head. An initial segmentation is computed using an unsupervised clustering algorithm. Then, integrated domain knowledge and image processing techniques contribute to the final tumor segmentation. The system was trained on two patient volumes and preliminary testing has shown successful tumor segmentations on four patient volumes.

  12. Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

    PubMed

    Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

    2009-09-01

    A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

  13. Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

    PubMed

    Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

    2009-09-01

    A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms. PMID:19446435

  14. [Graph-based interactive three-dimensional segmentation of magnetic resonance images of brain tumors].

    PubMed

    Li, Wei; Chen, Wu-fan

    2009-01-01

    We propose a graph-based three-dimensional (3D) algorithm to automatically segment brain tumors from magnetic resonance images (MRI). The algorithm uses minimum s/t cut criteria to obtain a global optimal result of objective function formed according to Markov Random Field Model and Maximum a posteriori (MAP-MRF) theory, and by combining the expectation-maximization (EM) algorithm to estimate the parameters of mixed Gaussian model for normal brain and tumor tissues. 3D segmentation results of brain tumors are fast achieved by our algorithm. The validation of the algorithm was tested and showed good accuracy and adaptation under simple interactions with the physicians. PMID:19218135

  15. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    PubMed

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

  16. Multi-fractal texture features for brain tumor and edema segmentation

    NASA Astrophysics Data System (ADS)

    Reza, S.; Iftekharuddin, K. M.

    2014-03-01

    In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

  17. Semi-automatic segmentation of brain tumors using population and individual information.

    PubMed

    Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

    2013-08-01

    Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation. PMID:23319111

  18. Segmentation of brain tumors in 4D MR images using the hidden Markov model.

    PubMed

    Solomon, Jeffrey; Butman, John A; Sood, Arun

    2006-12-01

    Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone. PMID:17050032

  19. MRI brain tumor segmentation and necrosis detection using adaptive Sobolev snakes

    NASA Astrophysics Data System (ADS)

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-03-01

    Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at di erent points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D di usion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

  20. [RSF model optimization and its application to brain tumor segmentation in MRI].

    PubMed

    Cheng, Zhaoning; Song, Zhijian

    2013-04-01

    Magnetic resonance imaging (MRI) is usually obscure and non-uniform in gray, and the tumors inside are poorly circumscribed, hence the automatic tumor segmentation in MRI is very difficult. Region-scalable fitting (RSF) energy model is a new segmentation approach for some uneven grayscale images. However, the level set formulation (LSF) of RSF model is not suitable for the environment with different grey level distribution inside and outside the intial contour, and the complex intensity environment of MRI always makes it hard to get ideal segmentation results. Therefore, we improved the model by a new LSF and combined it with the mean shift method, which can be helpful for tumor segmentation and has better convergence and target direction. The proposed method has been utilized in a series of studies for real MRI images, and the results showed that it could realize fast, accurate and robust segmentations for brain tumors in MRI, which has great clinical significance. PMID:23858745

  1. A novel content-based active contour model for brain tumor segmentation.

    PubMed

    Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

    2012-06-01

    Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. PMID:22459443

  2. Brain tumor segmentation in MR slices using improved GrowCut algorithm

    NASA Astrophysics Data System (ADS)

    Ji, Chunhong; Yu, Jinhua; Wang, Yuanyuan; Chen, Liang; Shi, Zhifeng; Mao, Ying

    2015-12-01

    The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.

  3. Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.

    PubMed

    Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

    2016-01-01

    Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments. PMID:27001047

  4. Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

    NASA Astrophysics Data System (ADS)

    Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

    2016-03-01

    Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83–0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

  5. 3D Slicer as a Tool for Interactive Brain Tumor Segmentation

    PubMed Central

    Kikinis, Ron; Pieper, Steve

    2014-01-01

    User interaction is required for reliable segmentation of brain tumors in clinical practice and in clinical research. By incorporating current research tools, 3D Slicer provides a set of easy to use interactive tools that can be efficiently used for this purpose. PMID:22255945

  6. Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

    PubMed

    Demirhan, Ayşe; Toru, Mustafa; Guler, Inan

    2015-07-01

    Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.

  7. Brain tumor evaluation and segmentation by in vivo proton spectroscopy and relaxometry.

    PubMed

    Martín-Landrove, Miguel; Mayobre, Finita; Bautista, Igor; Villalta, Raúl

    2005-12-01

    A new methodology has been developed for the evaluation and segmentation of brain tumors using information obtained by different magnetic resonance techniques such as in vivo proton magnetic resonance spectroscopy (1HMRS) and relaxometry. In vivo 1HMRS may be used as a preoperative technique that allows noninvasive monitoring of metabolites to identify the different tissue types present in the lesion (active tumor, necrotic tissue, edema, and normal or non-affected tissue). Spatial resolution for treatment consideration may be improved by using 1HMRS combined or fused with images obtained by relaxometry which exhibit excellent spatial resolution. Some segmentation schemes are presented and discussed. The results show that segmentation performed in this way efficiently determines the spatial localization of the tumor both qualitatively and quantitatively. It provides appropriate information for therapy planning and application of therapies such as radiosurgery or radiotherapy and future control of patient evolution.

  8. Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps.

    PubMed

    Vijayakumar, C; Damayanti, Gharpure; Pant, R; Sreedhar, C M

    2007-10-01

    An accurate computer-assisted method to perform segmentation of brain tumor on apparent diffusion coefficient (ADC) images and evaluate its grade (malignancy state) has been designed using a mixture of unsupervised artificial neural networks (ANN) and hierarchical multiresolution wavelet. Firstly, the ADC images are decomposed by multiresolution wavelets, which are subsequently selectively reconstructed to form wavelet filtered images. These wavelet filtered images along with FLAIR and T2 weighted images have been utilized as the features to unsupervised neural network - self organizing maps (SOM) - to segment the tumor, edema, necrosis, CSF and normal tissue and grade the malignant state of the tumor. A novel segmentation algorithm based on the number of hits experienced by Best Matching Units (BMU) on SOM maps is proposed. The results shows that the SOM performs well in differentiating the tumor, edema, necrosis, CSF and normal tissue pattern vectors on ADC images. Using the trained SOM and proposed segmentation algorithm, we are able to identify high or low grade tumor, edema, necrosis, CSF and normal tissue. The results are validated against manually segmented images and sensitivity and the specificity are observed to be 0.86 and 0.93, respectively. PMID:17572068

  9. Joint segmentation and deformable registration of brain scans guided by a tumor growth model.

    PubMed

    Gooya, Ali; Pohl, Kilian M; Bilello, Michel; Biros, George; Davatzikos, Christos

    2011-01-01

    This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth.

  10. Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

    PubMed

    Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

  11. Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

    PubMed

    Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064

  12. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior

    PubMed Central

    Lu, Yisu; Jiang, Jun; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064

  13. Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

    PubMed

    Zhao, Liang; Wu, Wei; Corso, Jason J

    2013-01-01

    Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories-the medical image analog to optical flow and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation. PMID:24505807

  14. Automatic brain tumor segmentation with a fast Mumford-Shah algorithm

    NASA Astrophysics Data System (ADS)

    Müller, Sabine; Weickert, Joachim; Graf, Norbert

    2016-03-01

    We propose a fully-automatic method for brain tumor segmentation that does not require any training phase. Our approach is based on a sequence of segmentations using the Mumford-Shah cartoon model with varying parameters. In order to come up with a very fast implementation, we extend the recent primal-dual algorithm of Strekalovskiy et al. (2014) from the 2D to the medically relevant 3D setting. Moreover, we suggest a new confidence refinement and show that it can increase the precision of our segmentations substantially. Our method is evaluated on 188 data sets with high-grade gliomas and 25 with low-grade gliomas from the BraTS14 database. Within a computation time of only three minutes, we achieve Dice scores that are comparable to state-of-the-art methods.

  15. Development of image-processing software for automatic segmentation of brain tumors in MR images.

    PubMed

    Vijayakumar, C; Gharpure, Damayanti Chandrashekhar

    2011-07-01

    Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called 'Prometheus,' which performs neural system-based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively. PMID:21897560

  16. Brain Tumors

    MedlinePlus

    A brain tumor is a growth of abnormal cells in the tissues of the brain. Brain tumors can be benign, with no cancer cells, ... cancer cells that grow quickly. Some are primary brain tumors, which start in the brain. Others are ...

  17. Segmentation of brain tumors in MRI images using multi-scale gradient vector flow.

    PubMed

    Kazerooni, Anahita Fathi; Ahmadian, Alireza; Serej, Nassim Dadashi; Rad, Hamidreza Saligheh; Saberi, Hooshang; Yousefi, Hossein; Farnia, Parastoo

    2011-01-01

    The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI images. Using this idea, the active contour is evolved with respect to scaled edge maps in a multi scale manner. The edge detection performance of the modified GVF algorithm is further enhanced by applying a threshold-based edge detector to improve the edge map. The Bspline snake is selected for representation of the active contour, due to its ability to capture corners and its local control. The results showed an improvement of 30% in the accuracy of tumor segmentation against traditional GVF and 10 % as compared to Bspline GVF in the presence of noise, besides the repeatability of the algorithm in contrast to traditional GVF. The clinical evaluation also proved the accuracy and sensitivity of the proposed method as 92.8% and 95.4%, respectively. PMID:22256190

  18. Brain tumors.

    PubMed Central

    Black, K. L.; Mazziotta, J. C.; Becker, D. P.

    1991-01-01

    Recent advances in experimental tumor biology are being applied to critical clinical problems of primary brain tumors. The expression of peripheral benzodiazepine receptors, which are sparse in normal brain, is increased as much as 20-fold in brain tumors. Experimental studies show promise in using labeled ligands to these receptors to identify the outer margins of malignant brain tumors. Whereas positron emission tomography has improved the dynamic understanding of tumors, the labeled selective tumor receptors with positron emitters will enhance the ability to specifically diagnose and greatly aid in the pretreatment planning for tumors. Modulation of these receptors will also affect tumor growth and metabolism. Novel methods to deliver antitumor agents to the brain and new approaches using biologic response modifiers also hold promise to further improve the management of brain tumors. Images PMID:1848735

  19. Brain tumor target volume determination for radiation therapy treatment planning through the use of automated MRI segmentation

    NASA Astrophysics Data System (ADS)

    Mazzara, Gloria Patrika

    Radiation therapy seeks to effectively irradiate the tumor cells while minimizing the dose to adjacent normal cells. Prior research found that the low success rates for treating brain tumors would be improved with higher radiation doses to the tumor area. This is feasible only if the target volume can be precisely identified. However, the definition of tumor volume is still based on time-intensive, highly subjective manual outlining by radiation oncologists. In this study the effectiveness of two automated Magnetic Resonance Imaging (MRI) segmentation methods, k-Nearest Neighbors (kNN) and Knowledge-Guided (KG), in determining the Gross Tumor Volume (GTV) of brain tumors for use in radiation therapy was assessed. Three criteria were applied: accuracy of the contours; quality of the resulting treatment plan in terms of dose to the tumor; and a novel treatment plan evaluation technique based on post-treatment images. The kNN method was able to segment all cases while the KG method was limited to enhancing tumors and gliomas with clear enhancing edges. Various software applications were developed to create a closed smooth contour that encompassed the tumor pixels from the segmentations and to integrate these results into the treatment planning software. A novel, probabilistic measurement of accuracy was introduced to compare the agreement of the segmentation methods with the weighted average physician volume. Both computer methods under-segment the tumor volume when compared with the physicians but performed within the variability of manual contouring (28% +/- 12% for inter-operator variability). Computer segmentations were modified vertically to compensate for their under-segmentation. When comparing radiation treatment plans designed from physician-defined tumor volumes with treatment plans developed from the modified segmentation results, the reference target volume was irradiated within the same level of conformity. Analysis of the plans based on post

  20. Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration.

    PubMed

    Bauer, Stefan; Seiler, Christof; Bardyn, Thibaut; Buechler, Philippe; Reyes, Mauricio

    2010-01-01

    We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

  1. Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors.

    PubMed

    Sanjuán, Ana; Price, Cathy J; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K; Geva, Sharon; Leff, Alex P; Yousry, Tarek A; Seghier, Mohamed L

    2013-01-01

    Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit "extra prior" for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic.

  2. Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

    PubMed Central

    Sanjuán, Ana; Price, Cathy J.; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K.; Geva, Sharon; Leff, Alex P.; Yousry, Tarek A.; Seghier, Mohamed L.

    2013-01-01

    Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic. PMID:24381535

  3. 3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.

    PubMed

    Jiang, Jun; Wu, Yao; Huang, Meiyan; Yang, Wei; Chen, Wufan; Feng, Qianjin

    2013-01-01

    Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. PMID:23816459

  4. Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation.

    PubMed

    Mehmood, Irfan; Ejaz, Naveed; Sajjad, Muhammad; Baik, Sung Wook

    2013-10-01

    The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists. PMID:24034739

  5. Understanding Brain Tumors

    MedlinePlus

    ... to Know About Brain Tumors . What is a Brain Tumor? A brain tumor is an abnormal growth
 ... Tumors” from Frankly Speaking Frankly Speaking About Cancer: Brain Tumors Download the full book Questions to ask ...

  6. Brain Tumor Diagnosis

    MedlinePlus

    ... Types of Brain Scans X-rays Laboratory Tests DNA Profiling Biopsy Procedure Malignant and Benign Brain Tumors Tumor ... Types of Brain Scans X-rays Laboratory Tests DNA Profiling Biopsy Procedure Malignant and Benign Brain Tumors Tumor ...

  7. Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies

    SciTech Connect

    Weizman, Lior; Sira, Liat Ben; Joskowicz, Leo; Rubin, Daniel L.; Yeom, Kristen W.; Constantini, Shlomi; Shofty, Ben; Bashat, Dafna Ben

    2014-05-15

    Purpose: Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans. Methods: The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components. Tumor components gray level parameters are estimated from the follow-up scan itself, obviating temporal normalization of gray levels. The tumor delineation procedure effectively incorporates internal classification of the baseline scan in the time-series as prior data to segment and classify a series of follow-up scans. The authors applied their method to 40 MRI scans of ten patients, acquired at two different institutions. Two types of LGTs were included: Optic pathway gliomas and thalamic astrocytomas. For each scan, a “gold standard” was obtained manually by experienced radiologists. The method is evaluated versus the gold standard with three measures: gross total volume error, total surface distance, and reliability of tracking tumor components evolution. Results: Compared to the gold standard the authors' method exhibits a mean Dice similarity volumetric measure of 86.58% and a mean surface distance error of 0.25 mm. In terms of its reliability in tracking the evolution of the internal components, the method exhibits strong positive correlation with the gold standard. Conclusions: The authors' method provides accurate and repeatable delineation of the tumor and its internal components, which is essential for therapy assessment of LGTs. Reliable tracking of internal tumor components over time is novel and potentially will be useful to streamline and improve follow-up of brain tumors, with indolent growth and behavior.

  8. Brain tumor (image)

    MedlinePlus

    Brain tumors are classified depending on the exact site of the tumor, the type of tissue involved, benign ... tendencies of the tumor, and other factors. Primary brain tumors can arise from the brain cells, the meninges ( ...

  9. Brain Tumors (For Parents)

    MedlinePlus

    ... Story" 5 Things to Know About Zika & Pregnancy Brain Tumors KidsHealth > For Parents > Brain Tumors Print A ... radiation therapy or chemotherapy, or both. Types of Brain Tumors There are many different types of brain ...

  10. Brain Tumor Symptoms

    MedlinePlus

    ... Types of Tumors Risk Factors Brain Tumor Statistics Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board of Directors Staff ... Types of Tumors Risk Factors Brain Tumor Statistics Brain Tumor Dictionary Webinars Anytime Learning Donate to the ABTA Help advance the understanding ...

  11. Childhood Brain Tumors

    MedlinePlus

    Brain tumors are abnormal growths inside the skull. They are among the most common types of childhood ... still be serious. Malignant tumors are cancerous. Childhood brain and spinal cord tumors can cause headaches and ...

  12. Brain Tumor Statistics

    MedlinePlus

    ... facts and statistics here include brain and central nervous system tumors (including spinal cord, pituitary and pineal gland ... U.S. living with a primary brain and central nervous system tumor. This year, nearly 17,000 people will ...

  13. Children's Brain Tumor Foundation

    MedlinePlus

    ... CBTF Justin's Hope Fund Grant Recipients Grants Children’s Brain Tumor Foundation, A non-profit organization, was founded ... and the long term outlook for children with brain and spinal cord tumors through research, support, education, ...

  14. Pediatric Brain Tumor Foundation

    MedlinePlus

    ... you insights into your child's treatment. LEARN MORE Brain tumors and their treatment can be deadly so ... Cancer Foundation joins the PBTF Read more >> Pediatric Brain Tumor Foundation 302 Ridgefield Court, Asheville, NC 28806 ...

  15. American Brain Tumor Association

    MedlinePlus

    ... in the Ear Canals Read More ABTA News October 5, 2016 Largest American Brain Tumor Association Team Running in Bank of America Chicago Marathon Sunday, October 9 September 21, 2016 American Brain Tumor Association Awards 16 Grants to Support ...

  16. Brain and Spinal Tumors

    MedlinePlus

    ... Awards Enhancing Diversity Find People About NINDS NINDS Brain and Spinal Tumors Information Page Synonym(s): Spinal Cord ... en Español Additional resources from MedlinePlus What are Brain and Spinal Tumors? Tumors of the brain and ...

  17. Automated Tumor Volumetry Using Computer-Aided Image Segmentation

    PubMed Central

    Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

    2015-01-01

    Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

  18. Brain tumor - primary - adults

    MedlinePlus

    ... tumor, relieve symptoms, and improve brain function or comfort. Surgery is often needed for most primary brain ... and pressure Anticonvulsants to reduce seizures Pain medicines Comfort measures, safety measures, physical therapy, and occupational therapy ...

  19. Brain tumor - children

    MedlinePlus

    ... symptoms, and improve brain function or the child's comfort. Surgery is needed for most primary brain tumors. ... Anticonvulsants to reduce or prevent seizures Pain medicines Comfort measures, safety measures, physical therapy, occupational therapy, and ...

  20. Radioresistance of Brain Tumors

    PubMed Central

    Kelley, Kevin; Knisely, Jonathan; Symons, Marc; Ruggieri, Rosamaria

    2016-01-01

    Radiation therapy (RT) is frequently used as part of the standard of care treatment of the majority of brain tumors. The efficacy of RT is limited by radioresistance and by normal tissue radiation tolerance. This is highlighted in pediatric brain tumors where the use of radiation is limited by the excessive toxicity to the developing brain. For these reasons, radiosensitization of tumor cells would be beneficial. In this review, we focus on radioresistance mechanisms intrinsic to tumor cells. We also evaluate existing approaches to induce radiosensitization and explore future avenues of investigation. PMID:27043632

  1. Modern Brain Tumor Imaging

    PubMed Central

    Barajas, Ramon F.; Cha, Soonmee

    2015-01-01

    The imaging and clinical management of patients with brain tumor continue to evolve over time and now heavily rely on physiologic imaging in addition to high-resolution structural imaging. Imaging remains a powerful noninvasive tool to positively impact the management of patients with brain tumor. This article provides an overview of the current state-of-the art clinical brain tumor imaging. In this review, we discuss general magnetic resonance (MR) imaging methods and their application to the diagnosis of, treatment planning and navigation, and disease monitoring in patients with brain tumor. We review the strengths, limitations, and pitfalls of structural imaging, diffusion-weighted imaging techniques, MR spectroscopy, perfusion imaging, positron emission tomography/MR, and functional imaging. Overall this review provides a basis for understudying the role of modern imaging in the care of brain tumor patients. PMID:25977902

  2. Automated segmentation of ventricles from serial brain MRI for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

    NASA Astrophysics Data System (ADS)

    Pura, John A.; Hamilton, Allison M.; Vargish, Geoffrey A.; Butman, John A.; Linguraru, Marius George

    2011-03-01

    Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1- weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.

  3. Brain tumor stem cells.

    PubMed

    Palm, Thomas; Schwamborn, Jens C

    2010-06-01

    Since the end of the 'no-new-neuron' theory, emerging evidence from multiple studies has supported the existence of stem cells in neurogenic areas of the adult brain. Along with this discovery, neural stem cells became candidate cells being at the origin of brain tumors. In fact, it has been demonstrated that molecular mechanisms controlling self-renewal and differentiation are shared between brain tumor stem cells and neural stem cells and that corruption of genes implicated in these pathways can direct tumor growth. In this regard, future anticancer approaches could be inspired by uncovering such redundancies and setting up treatments leading to exhaustion of the cancer stem cell pool. However, deleterious effects on (normal) neural stem cells should be minimized. Such therapeutic models underline the importance to study the cellular mechanisms implicated in fate decisions of neural stem cells and the oncogenic derivation of adult brain cells. In this review, we discuss the putative origins of brain tumor stem cells and their possible implications on future therapies.

  4. Aquaporins and Brain Tumors

    PubMed Central

    Maugeri, Rosario; Schiera, Gabriella; Di Liegro, Carlo Maria; Fricano, Anna; Iacopino, Domenico Gerardo; Di Liegro, Italia

    2016-01-01

    Brain primary tumors are among the most diverse and complex human cancers, and they are normally classified on the basis of the cell-type and/or the grade of malignancy (the most malignant being glioblastoma multiforme (GBM), grade IV). Glioma cells are able to migrate throughout the brain and to stimulate angiogenesis, by inducing brain capillary endothelial cell proliferation. This in turn causes loss of tight junctions and fragility of the blood–brain barrier, which becomes leaky. As a consequence, the most serious clinical complication of glioblastoma is the vasogenic brain edema. Both glioma cell migration and edema have been correlated with modification of the expression/localization of different isoforms of aquaporins (AQPs), a family of water channels, some of which are also involved in the transport of other small molecules, such as glycerol and urea. In this review, we discuss relationships among expression/localization of AQPs and brain tumors/edema, also focusing on the possible role of these molecules as both diagnostic biomarkers of cancer progression, and therapeutic targets. Finally, we will discuss the possibility that AQPs, together with other cancer promoting factors, can be exchanged among brain cells via extracellular vesicles (EVs). PMID:27367682

  5. Drugs Approved for Brain Tumors

    MedlinePlus

    ... Ask about Your Treatment Research Drugs Approved for Brain Tumors This page lists cancer drugs approved by ... that are not listed here. Drugs Approved for Brain Tumors Afinitor (Everolimus) Afinitor Disperz (Everolimus) Avastin (Bevacizumab) ...

  6. Brain tumors in infants

    PubMed Central

    Ghodsi, Seyyed Mohammad; Habibi, Zohreh; Hanaei, Sara; Moradi, Ehsan; Nejat, Farideh

    2015-01-01

    Background: Brain tumors in infants have different clinical presentations, anatomical distribution, histopathological diagnosis, and clinical prognosis compared with older children. Materials and Methods: A retrospective analysis was done in patients <12 months old who were operated on for primary brain tumor in Children's Hospital Medical Center since 2008 to 2014. Results: Thirty-one infants, 20 males and 11 females, with the mean age of 7.13 months (0.5–12) were enrolled. There were 16 supratentorial and 15 infratentorial tumors. The presenting symptoms included increased head circumference (16); bulge fontanel (15); vomiting (15); developmental regression (11); sunset eye (7); seizure (4); loss of consciousness (4); irritability (3); nystagmus (2); visual loss (2); hemiparesis (2); torticollis (2); VI palsy (3); VII, IX, X nerve palsy (each 2); and ptosis (1). Gross total and subtotal resection were performed in 19 and 11 cases, respectively. Fourteen patients needed external ventricular drainage in the perioperative period, from whom four infants required a ventriculoperitoneal shunt. One patient underwent ventriculoperitoneal shunting without tumor resection. The most common histological diagnoses were primitive neuroectodermal tumor (7), followed by anaplastic ependymoma (6) and grade II ependymoma. The rate of 30-day mortality was 19.3%. Eighteen patients are now well-controlled with or without adjuvant therapy (overall survival; 58%), from whom 13 cases are tumor free (disease free survival; 41.9%), 3 cases have residual masses with fixed or decreased size (progression-free survival; 9.6%), and 2 cases are still on chemotherapy. Conclusion: Brain tumors in infants should be treated with surgical resection, followed by chemotherapy when necessary. PMID:26962338

  7. [Chemotherapy of brain tumors].

    PubMed

    Kuratsu, J; Ushio, Y

    1994-10-01

    Despite recent attempts to improve chemotherapeutic approaches for the treatment of malignant gliomas, results remain limited and palliative. The development of effective chemotherapy for tumors of the central nervous system (CNS) is complicated in that the blood-brain barrier (B.B.B.) hampers the penetration of most drugs into the brain and cerebrospinal fluid. The factors governing delivery in the brain are the drug's molecular weight, lipophilicity and degree of ionization. Now the standard therapy for malignant glioma is maximal tumor resection followed by combination radiotherapy plus chemotherapy. Nitrosoureas are representative drugs which easily cross the B.B.B.. It has been shown that nitrosourea compounds have an additive effect to radiotherapy. The toxicity profile of nitrosoureas is leukocytopenia and thrombocytopenia as a dose-limiting factor. Furthermore, the great heterogeneity of malignant glioma tissues offered a rationale for the use of multiple drugs. Many studies were reported to show a substantial advantage for the multidrug regimen over control series utilizing single drugs alone. Despite clear examples of the effectiveness of chemotherapy, we are still far from improving the cure rate for the vast majority of patients with primary malignancies of the CNS. Further improvement in patient survival may depend upon understanding and manipulating the pathways that regulate aberrant growth in these tumors. The development of new anticancer agents, which are sensitive to malignant glioma and can reach a high concentration in glioma tissue, is warranted. PMID:7986118

  8. Brain Tumor Epidemiology Consortium (BTEC)

    Cancer.gov

    The Brain Tumor Epidemiology Consortium is an open scientific forum organized to foster the development of multi-center, international and inter-disciplinary collaborations that will lead to a better understanding of the etiology, outcomes, and prevention of brain tumors.

  9. Intra-axial brain tumors.

    PubMed

    Rapalino, Otto; Batchelor, Tracy; González, R Gilberto

    2016-01-01

    There is a wide variety of intra-axial primary and secondary brain neoplasms. Many of them have characteristic imaging features while other tumors can present in a similar fashion. There are peculiar posttreatment imaging phenomena that can present as intra-axial mass-like lesions (such as pseudoprogression or radiation necrosis), further complicating the diagnosis and clinical follow-up of patients with intracerebral tumors. The purpose of this chapter is to present a general overview of the most common intra-axial brain tumors and peculiar posttreatment changes that are very important in the diagnosis and clinical follow-up of patients with brain tumors. PMID:27432670

  10. Pediatric brain tumors and epilepsy.

    PubMed

    Wells, Elizabeth M; Gaillard, William D; Packer, Roger J

    2012-03-01

    Seizures are a common complication of pediatric brain tumors and their treatment. This article reviews the epidemiology, evaluation, and treatment of seizures in children with brain tumors. Seizures in known brain tumor patients may signify tumor progression or recurrence, or treatment-related brain damage, as well as other causes, including low drug levels and metabolic disturbances. Careful selection of antiepileptic medications is needed in this population. There are advantages to nonenzyme-inducing antiepileptic drugs including valproic acid, which has potential antitumoral properties as a histone deacetylase inhibitor. Tumor surgery cures many cases of pediatric tumor-associated seizures, and some children are controlled with anti-epileptic medication, however additional epilepsy surgery may be needed for refractory cases.

  11. Childhood Brain Tumor Epidemiology: A Brain Tumor Epidemiology Consortium Review

    PubMed Central

    Johnson, Kimberly J.; Cullen, Jennifer; Barnholtz-Sloan, Jill S.; Ostrom, Quinn T.; Langer, Chelsea E.; Turner, Michelle C.; McKean-Cowdin, Roberta; Fisher, James L.; Lupo, Philip J.; Partap, Sonia; Schwartzbaum, Judith A.; Scheurer, Michael E.

    2014-01-01

    Childhood brain tumors are the most common pediatric solid tumor and include several histological subtypes. Although progress has been made in improving survival rates for some subtypes, understanding of risk factors for childhood brain tumors remains limited to a few genetic syndromes and ionizing radiation to the head and neck. In this report, we review descriptive and analytical epidemiology childhood brain tumor studies from the past decade and highlight priority areas for future epidemiology investigations and methodological work that is needed to advance our understanding of childhood brain tumor causes. Specifically, we summarize the results of a review of studies published since 2004 that have analyzed incidence and survival in different international regions and that have examined potential genetic, immune system, developmental and birth characteristics, and environmental risk factors. PMID:25192704

  12. Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms.

    PubMed

    Strzelecki, Michal; Materka, Andrzej; Drozdz, Jaroslaw; Krzeminska-Pakula, Maria; Kasprzak, Jaroslaw D

    2006-03-01

    This paper describes an automatic method for classification and segmentation of different intracardiac masses in tumor echocardiograms. Identification of mass type is highly desirable, since to different treatment options for cardiac tumors (surgical resection) and thrombi (effective anticoagulant treatment) are possible. Correct diagnosis of the character of intracardiac mass in a living patient is a true challenge for a cardiologist; therefore, an objective image analysis method may be useful in heart diseases diagnosis. Image texture analysis is used to distinguish various types of masses. The presented methods assume that image texture encodes important histological features of masses and, therefore, texture numerical parameters enable the discrimination and segmentation of a mass. The recently developed technique based on the network of synchronized oscillators is proposed for the image segmentation. This technique is based on a 'temporary correlation' theory, which attempts to explain scene recognition as it would be performed by a human brain. This theory assumes that different groups of neural cells encode different properties of homogeneous image regions (e.g. shape, color, texture). Monitoring of temporal activity of cell groups leads to scene segmentation. A network of synchronized oscillators was successfully used for segmentation of Brodatz textures and medical textured images. The advantage of this network is its ability to detect texture boundaries. It can be also manufactured as a VLSI chip, for a very fast image segmentation. The accuracy of locating of analyzed tissues in the image should be assessed to evaluate a segmentation technique. The new evaluation method based on measurement of physical textured test objects was proposed. Firstly, a series of object images was obtained by the use of different devices (scanner, digital camera and TV camera). Secondly, the images were segmented using oscillator network and feedforward artificial neural

  13. Radiosurgery for Pediatric Brain Tumors.

    PubMed

    Murphy, Erin S; Chao, Samuel T; Angelov, Lilyana; Vogelbaum, Michael A; Barnett, Gene; Jung, Edward; Recinos, Violette R; Mohammadi, Alireza; Suh, John H

    2016-03-01

    The utility of radiosurgery for pediatric brain tumors is not well known. For children, radiosurgery may have an important role for treating unresectable tumors, residual disease, or tumors in the recurrent setting that have received prior radiotherapy. The available evidence demonstrates utility for some children with primary brain tumors resulting in good local control. Radiosurgery can be considered for limited residual disease or focal recurrences. However, the potential toxicities are unique and not insignificant. Therefore, prospective studies need to be performed to develop guidelines for indications and treatment for children and reduce toxicity in this population. PMID:26536284

  14. Segmentation of liver region with tumorous tissues

    NASA Astrophysics Data System (ADS)

    Zhang, Xuejun; Lee, Gobert; Tajima, Tetsuji; Kitagawa, Teruhiko; Kanematsu, Masayuki; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kondo, Hiroshi; Hoshi, Hiroaki; Nawano, Shigeru; Shinozaki, Kenji

    2007-03-01

    Segmentation of an abnormal liver region based on CT or MR images is a crucial step in surgical planning. However, precisely carrying out this step remains a challenge due to either connectivities of the liver to other organs or the shape, internal texture, and homogeneity of liver that maybe extensively affected in case of liver diseases. Here, we propose a non-density based method for extracting the liver region containing tumor tissues by edge detection processing. False extracted regions are eliminated by a shape analysis method and thresholding processing. If the multi-phased images are available then the overall outcome of segmentation can be improved by subtracting two phase images, and the connectivities can be further eliminated by referring to the intensity on another phase image. Within an edge liver map, tumor candidates are identified by their different gray values relative to the liver. After elimination of the small and nonspherical over-extracted regions, the final liver region integrates the tumor region with the liver tissue. In our experiment, 40 cases of MDCT images were used and the result showed that our fully automatic method for the segmentation of liver region is effective and robust despite the presence of hepatic tumors within the liver.

  15. A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

    NASA Astrophysics Data System (ADS)

    Agn, Mikael; Law, Ian; Munck af Rosenschöld, Per; Van Leemput, Koen

    2016-03-01

    We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method.

  16. Brain and Spinal Cord Tumors in Adults

    MedlinePlus

    ... saved articles window. My Saved Articles » My ACS » Brain and Spinal Cord Tumors in Adults Download Printable ... the topics below to get started. What Is Brain/CNS Tumors In Adults? What are adult brain ...

  17. Metastatic brain tumor

    MedlinePlus

    ... be to relieve symptoms, improve functioning, or provide comfort. Radiation to the whole brain is often used ... symptoms. This is called palliative or supportive care. Comfort measures, safety measures, physical therapy, occupational therapy, and ...

  18. Cytogenetics of human brain tumors

    SciTech Connect

    Finkernagel, S.W.; Kletz, T.; Day-Salvatore, D.L.

    1994-09-01

    Chromosome studies of 55 brain tumors, including meningiomas, gliomas, astrocyomas and pituatary adenomas, were performed. Primary and first passage cultures were successfully obtained in 75% of these samples with an average of 18 G-banded metaphases analyzed per tumor. 44% of all the brain tumors showed numerical and or structural abnormalities. 46% of the primary and 38% of the first passage cultures showed similar numerical gains/losses and complex karyotypic changes. The most frequent numerical abnormalities (n {ge} 5) included loss of chromosomes 10, 22, and Y. The structural abnormalities most often seen involved 1p, 2, 5, 7, 17q and 19. This is an ongoing study which will attempt to correlate tumor type with specific karyotypic changes and to see if any of the observed chromosomal abnormalities provide prognostic indicators.

  19. Brain tumors in irradiated monkeys.

    NASA Technical Reports Server (NTRS)

    Haymaker, W.; Miquel, J.; Rubinstein, L. J.

    1972-01-01

    A study was made of 32 monkeys which survived one to seven years after total body exposure to protons or to high-energy X rays. Among these 32 monkeys there were 21 which survived two years or longer after exposure to 200 to 800 rad. Glioblastoma multiforme developed in 3 of the 10 monkeys surviving three to five years after receiving 600 or 800 rad 55-MeV protons. Thus, the incidence of tumor development in the present series was far higher than the incidence of spontaneously developing brain tumors in monkeys cited in the literature. This suggests that the tumors in the present series may have been radiation-induced.

  20. Robust whole-brain segmentation: application to traumatic brain injury.

    PubMed

    Ledig, Christian; Heckemann, Rolf A; Hammers, Alexander; Lopez, Juan Carlos; Newcombe, Virginia F J; Makropoulos, Antonios; Lötjönen, Jyrki; Menon, David K; Rueckert, Daniel

    2015-04-01

    We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to

  1. Unsupervised brain segmentation using T2 window

    NASA Astrophysics Data System (ADS)

    Alyassin, Abdal M.; Cline, Harvey E.

    2002-05-01

    Measurement of brain structures could lead to important diagnostic information and could indicate the success or failure of a certain pharmaceutical drug. We have developed a totally unsupervised technique that segments and quantifies brain structures from T2 dual echo MR images. The technique classified four different tissue clusters in a scatter plot (air, CSF, brain, and face). Several novel image-processing techniques were implemented to reduce the spread of these clusters and subsequently generate tissue based T2 windows. These T2 windows encompassed all the information needed to segment and subsequently quantify the corresponding tissues in an automatic fashion. We have applied the technique on nineteen MR data sets (16 normal and 3 Alzheimer diseased [AD] patients). The measurements from the T2 window technique differentiated AD patients from normal subjects. The mean value of the %CSF from total the brain was %29.2 higher for AD patients from the %CSF for normal subjects. Furthermore, the technique ran under 30 seconds per data set on a PC with 550 MHz dual processors.

  2. More Complete Removal of Malignant Brain Tumors by Fluorescence-Guided Surgery

    ClinicalTrials.gov

    2016-05-13

    Benign Neoplasms, Brain; Brain Cancer; Brain Neoplasms, Benign; Brain Neoplasms, Malignant; Brain Tumor, Primary; Brain Tumor, Recurrent; Brain Tumors; Intracranial Neoplasms; Neoplasms, Brain; Neoplasms, Intracranial; Primary Brain Neoplasms; Primary Malignant Brain Neoplasms; Primary Malignant Brain Tumors; Gliomas; Glioblastoma

  3. Automatic brain segmentation in rhesus monkeys

    NASA Astrophysics Data System (ADS)

    Styner, Martin; Knickmeyer, Rebecca; Joshi, Sarang; Coe, Christopher; Short, Sarah J.; Gilmore, John

    2007-03-01

    Many neuroimaging studies are applied to primates as pathologies and environmental exposures can be studied in well-controlled settings and environment. In this work, we present a framework for both the semi-automatic creation of a rhesus monkey atlas and a fully automatic segmentation of brain tissue and lobar parcellation. We determine the atlas from training images by iterative, joint deformable registration into an unbiased average image. On this atlas, probabilistic tissue maps and a lobar parcellation. The atlas is then applied via affine, followed by deformable registration. The affinely transformed atlas is employed for a joint T1/T2 based tissue classification. The deformed atlas parcellation masks the tissue segmentations to define the parcellation. Other regional definitions on the atlas can also straightforwardly be used as segmentation. We successfully built average atlas images for the T1 and T2 datasets using a developmental training datasets of 18 cases aged 16-34 months. The atlas clearly exhibits an enhanced signal-to-noise ratio compared to the original images. The results further show that the cortical folding variability in our data is highly limited. Our segmentation and parcellation procedure was successfully re-applied to all training images, as well as applied to over 100 additional images. The deformable registration was able to identify corresponding cortical sulcal borders accurately. Even though the individual methods used in this segmentation framework have been applied before on human data, their combination is novel, as is their adaptation and application to rhesus monkey MRI data. The reduced variability present in the primate data results in a segmentation pipeline that exhibits high stability and anatomical accuracy.

  4. Deregulated proliferation and differentiation in brain tumors

    PubMed Central

    Swartling, Fredrik J; Čančer, Matko; Frantz, Aaron; Weishaupt, Holger; Persson, Anders I

    2014-01-01

    Neurogenesis, the generation of new neurons, is deregulated in neural stem cell (NSC)- and progenitor-derived murine models of malignant medulloblastoma and glioma, the most common brain tumors of children and adults, respectively. Molecular characterization of human malignant brain tumors, and in particular brain tumor stem cells (BTSCs), has identified neurodevelopmental transcription factors, microRNAs, and epigenetic factors known to inhibit neuronal and glial differentiation. We are starting to understand how these factors are regulated by the major oncogenic drivers in malignant brain tumors. In this review, we will focus on the molecular switches that block normal neuronal differentiation and induce brain tumor formation. Genetic or pharmacological manipulation of these switches in BTSCs has been shown to restore the ability of tumor cells to differentiate. We will discuss potential brain tumor therapies that will promote differentiation in order to reduce treatment-resistance, suppress tumor growth, and prevent recurrence in patients. PMID:25416506

  5. Automatic segmentation of brain images: selection of region extraction methods

    NASA Astrophysics Data System (ADS)

    Gong, Leiguang; Kulikowski, Casimir A.; Mezrich, Reuben S.

    1991-07-01

    In automatically analyzing brain structures from a MR image, the choice of low level region extraction methods depends on the characteristics of both the target object and the surrounding anatomical structures in the image. The authors have experimented with local thresholding, global thresholding, and other techniques, using various types of MR images for extracting the major brian landmarks and different types of lesions. This paper describes specifically a local- binary thresholding method and a new global-multiple thresholding technique developed for MR image segmentation and analysis. The initial testing results on their segmentation performance are presented, followed by a comparative analysis of the two methods and their ability to extract different types of normal and abnormal brain structures -- the brain matter itself, tumors, regions of edema surrounding lesions, multiple sclerosis lesions, and the ventricles of the brain. The analysis and experimental results show that the global multiple thresholding techniques are more than adequate for extracting regions that correspond to the major brian structures, while local binary thresholding is helpful for more accurate delineation of small lesions such as those produced by MS, and for the precise refinement of lesion boundaries. The detection of other landmarks, such as the interhemispheric fissure, may require other techniques, such as line-fitting. These experiments have led to the formulation of a set of generic computer-based rules for selecting the appropriate segmentation packages for particular types of problems, based on which further development of an innovative knowledge- based, goal directed biomedical image analysis framework is being made. The system will carry out the selection automatically for a given specific analysis task.

  6. Gene therapy for brain tumors.

    PubMed

    Bansal, K; Engelhard, H H

    2000-09-01

    "Gene therapy" can be defined as the transfer of genetic material into a patient's cells for therapeutic purposes. To date, a diverse and creative assortment of treatment strategies utilizing gene therapy have been devised, including gene transfer for modulating the immune system, enzyme prodrug ("suicide gene") therapy, oncolytic therapy, replacement/therapeutic gene transfer, and antisense therapy. For malignant glioma, gene-directed prodrug therapy using the herpes simplex virus thymidine kinase gene was the first gene therapy attempted clinically. A variety of different strategies have now been pursued experimentally and in clinical trials. Although, to date, gene therapy for brain tumors has been found to be reasonably safe, concerns still exist regarding issues related to viral delivery, transduction efficiency, potential pathologic response of the brain, and treatment efficacy. Improved viral vectors are being sought, and potential use of gene therapy in combination with other treatments is being investigated.

  7. What underlies the diversity of brain tumors?

    PubMed Central

    Swartling, Fredrik J.; Hede, Sanna-Maria; Weiss, William A.

    2012-01-01

    Glioma and medulloblastoma represent the most commonly occurring malignant brain tumors in adults and in children respectively. Recent genomic and transcriptional approaches present a complex group of diseases, and delineate a number of molecular subgroups within tumors that share a common histopathology. Differences in cells of origin, regional niches, developmental timing and genetic events all contribute to this heterogeneity. In an attempt to recapitulate the diversity of brain tumors, an increasing array of genetically engineered mouse models (GEMMs) has been developed. These models often utilize promoters and genetic drivers from normal brain development, and can provide insight into specific cells from which these tumors originate. GEMMs show promise in both developmental biology and developmental therapeutics. This review describes numerous murine brain tumor models in the context of normal brain development, and the potential for these animals to impact brain tumor research. PMID:23085857

  8. Brain angiogenesis: Mechanism and Therapeutic Intervention in Brain Tumors

    PubMed Central

    Kim, Woo-Young; Lee, Ho-Young

    2010-01-01

    Summary Formation of new blood vessels is required for growth and metastasis of all solid tumors. New blood vessels are established in tumors mainly through angiogenesis. Brain tumors in particular are highly angiogenic. Therefore, interventions designed to prevent angiogenesis may be effective at controlling brain tumors. Indeed, many recent findings from preclinical and clinical studies of antiangiogenic therapy for brain tumors showed that it is a promising approach to managing this deadly disease, especially when combined with other cytotoxic treatments. In this review, we summarize the basic characteristics of brain tumor angiogenesis and role of known angiogenic factors in regulating this angiogenesis, which can be targets of antiangiogenic therapy. We also discuss the current status of antiangiogenic therapy for brain tumors, the suggested mechanisms of this therapy, and the limitations of this strategy. PMID:19664069

  9. Interstitial irradiation of brain tumors: a review

    SciTech Connect

    Bernstein, M.; Gutin, P.H.

    1981-12-01

    As an adjuvant to surgery, radiation therapy has consistently proven to be the most successful form of treatment for primary and secondary malignant brain tumors and possibly for inoperable benign tumors. Because the risk of radiation necrosis of normal brain limits the amount of radiation that can be given by external beam therapy at conventional dose rates, interstitial radiation of brain tumors is a logical alternative treatment approach. We discuss the radiobiological advantages of low dose rate irradiation and intratumoral placement of sources that make interstitial irradiation an attractive treatment for brain tumors and review the history of clinical brachytherapy for intracranial neoplasia.

  10. Automated brain segmentation using neural networks

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent; Johnson, Hans; Andreasen, Nancy

    2006-03-01

    Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures such as the thalamus (0.825), caudate (0.745), and putamen (0.755). One of the inputs into the ANN is the apriori probability of a structure existing at a given location. In this previous work, the apriori probability information was generated in Talairach space using a piecewise linear registration. In this work we have increased the dimensionality of this registration using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. The output of the neural network determined if the voxel was defined as one of the N regions used for training. Training was performed using a standard back propagation algorithm. The ANN was trained on a set of 15 images for 750,000,000 iterations. The resulting ANN weights were then applied to 6 test images not part of the training set. Relative overlap calculated for each structure was 0.875 for the thalamus, 0.845 for the caudate, and 0.814 for the putamen. With the modifications on the neural net algorithm and the use of multi-dimensional registration, we found substantial improvement in the automated segmentation method. The resulting segmented structures are as reliable as manual raters and the output of the neural network can be used without additional rater intervention.

  11. MRI segmentation of the human brain: challenges, methods, and applications.

    PubMed

    Despotović, Ivana; Goossens, Bart; Philips, Wilfried

    2015-01-01

    Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

  12. A Unified Framework for Brain Segmentation in MR Images

    PubMed Central

    Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

    2015-01-01

    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

  13. How Are Brain and Spinal Cord Tumors in Children Diagnosed?

    MedlinePlus

    ... spinal cord tumors in children staged? How are brain and spinal cord tumors diagnosed in children? Brain ... resonance angiography (MRA) or computerized tomographic angiography (CTA). Brain or spinal cord tumor biopsy Imaging tests such ...

  14. Consistent cortical reconstruction and multi-atlas brain segmentation.

    PubMed

    Huo, Yuankai; Plassard, Andrew J; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

    2016-09-01

    Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely

  15. Consistent cortical reconstruction and multi-atlas brain segmentation.

    PubMed

    Huo, Yuankai; Plassard, Andrew J; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

    2016-09-01

    Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely

  16. [A brain tumor automatic assisted-diagnostic system based on medical image shape analysis].

    PubMed

    Wang, Li-Li; Yang, Jie

    2005-03-01

    This paper covers a brain tumor assisted diagnosis system based on medical image analysis. The system supplements the PACS functions such as display of medical images and database inquiry, segments slice in real-time using the algorithm of fuzzy region competition, extracts shape feature factors such as contour label, compactness, moment, Fourier Descriptor, chord length, radius and other medical data on the brain tumor image with irregular contour feature after segmentation and then feeds to Bayesian network in order to sort the brain tumor for the implementation of automatic assisted diagnosis. PMID:16011110

  17. Diagnosis and surgical treatment of childhood brain tumors.

    PubMed

    Wilson, C B

    1975-03-01

    As the most frequent solid tumor occurring in childhood, brain tumors constitute an important segment of pediatric oncology. Neurologic manifestations may be deceptively mild and easily overlooked or misinterpreted, particularly in the very young, because of the remarkable resiliency of the immature central nervous system and the skull's ability to expand throughout the pre-adolescent years. The majority of childhood tumors produce increased intracranial pressure, usually the consequence of obstructive hydrocephalus. Specific neurologic deficits correspond to the tumor's location. The posterior fossa harbors two-thirds of childhood tumors, and each of the four common tumors in this location produces a characteristic syndrome. Supratentorial tumors occupy the cerebral hemisphere, the suprasellar area, and the pineal gland. Diagnostic studies have reached a state of great sophistication and precise anatomical localization. Surgery, either alone or with adjuvant radiotherapy, cures no more than one-third of all tumors; for the remainder, it has a diagnostic and palliative role. The introduction of operative microsurgery has advanced the art, particularly in the surgical treatment of craniopharyngiomas and pinealomas, but any significant improvement in the treatment of brain tumors as a group seems unlikely to be achieved by surgery alone.

  18. Brain tumors at a nuclear facility.

    PubMed

    Reyes, M; Wilkinson, G S; Tietjen, G; Voelz, G L; Acquavella, J F; Bistline, R

    1984-10-01

    In response to an observed excess risk of brain tumor deaths among workers at the Rocky Flats Nuclear Facility (Colorado), a case-control study of all (n = 16) primary brain tumor deaths occurring among white males employed during 1952 through 1977 was conducted to investigate their relationship with occupational radiation/nonradiation exposures. For each case, four controls were individually matched on year of birth and period of employment. Although limited by a small number of cases, our study showed no statistically significant association between brain tumor death and exposure to internally deposited plutonium, external radiation, or other occupational risk factors. PMID:6491777

  19. Malignant metastatic carcinoid presenting as brain tumor

    PubMed Central

    Sundar, I. Vijay; Jain, S. K.; Kurmi, Dhrubajyoti; Sharma, Rakesh; Chopra, Sanjeev; Singhvi, Shashi

    2016-01-01

    Carcinoid tumors are rarely known to metastasise to the brain. It is even more rare for such patients to present with symptoms related to metastases as the initial and only symptom. We present a case of a 60-year-old man who presented with hemiparesis and imaging features suggestive of brain tumor. He underwent surgery and the histopathology revealed metastatic malignant lesion of neuroendocrine origin. A subsequent work up for the primary was negative. Patient was treated with adjuvant radiotherapy. We present this case to highlight the pathophysiological features, workup and treatment options of this rare disease and discuss the methods of differentiating it from more common brain tumors. PMID:27366273

  20. Combining multi-atlas segmentation with brain surface estimation

    NASA Astrophysics Data System (ADS)

    Huo, Yuankai; Carass, Aaron; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.; Landman, Bennett A.

    2016-03-01

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitation in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

  1. Combining Multi-atlas Segmentation with Brain Surface Estimation

    PubMed Central

    Carass, Aaron; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.; Landman, Bennett A.

    2016-01-01

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this “segmentation to surface to parcellation” strategy has shown limitations in various situations. In this work, we propose a novel “multi-atlas segmentation to surface” method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly

  2. Automated segmentation of three-dimensional MR brain images

    NASA Astrophysics Data System (ADS)

    Park, Jonggeun; Baek, Byungjun; Ahn, Choong-Il; Ku, Kyo Bum; Jeong, Dong Kyun; Lee, Chulhee

    2006-03-01

    Brain segmentation is a challenging problem due to the complexity of the brain. In this paper, we propose an automated brain segmentation method for 3D magnetic resonance (MR) brain images which are represented as a sequence of 2D brain images. The proposed method consists of three steps: pre-processing, removal of non-brain regions (e.g., the skull, meninges, other organs, etc), and spinal cord restoration. In pre-processing, we perform adaptive thresholding which takes into account variable intensities of MR brain images corresponding to various image acquisition conditions. In segmentation process, we iteratively apply 2D morphological operations and masking for the sequences of 2D sagittal, coronal, and axial planes in order to remove non-brain tissues. Next, final 3D brain regions are obtained by applying OR operation for segmentation results of three planes. Finally we reconstruct the spinal cord truncated during the previous processes. Experiments are performed with fifteen 3D MR brain image sets with 8-bit gray-scale. Experiment results show the proposed algorithm is fast, and provides robust and satisfactory results.

  3. Staging Childhood Brain and Spinal Cord Tumors

    MedlinePlus

    ... before the cancer is diagnosed and continue for months or years. Childhood brain and spinal cord tumors ... after treatment. Some cancer treatments cause side effects months or years after treatment has ended. These are ...

  4. Automatic MRI 2D brain segmentation using graph searching technique.

    PubMed

    Pedoia, Valentina; Binaghi, Elisabetta

    2013-09-01

    Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. PMID:23757180

  5. Three mutant genes cooperatively induce brain tumor formation in Drosophila malignant brain tumor.

    PubMed

    Riede, I

    1996-09-01

    The Drosophila melanogaster strain Malignant Brain Tumor reveals temperature-sensitive transformation of the larval brain tissue. Genetic analysis shows that three gene defects, spzMBT, yetiMBT, and tldMBT, cooperatively induce brain tumor formation. Whereas spz and tld belong to the genes inducing differentiation patterns in the embryo, yeti induces cell overgrowth. spzMBT-, yetiMBT-, and tldMBT-containing animals are larval lethal, whereas Malignant Brain Tumor is kept as a homozygous strain at a permissive temperature. This reveals that this tumor-forming strain is the result of a number of adaptive mutation events.

  6. Surgical Outcomes of Hemorrhagic Metastatic Brain Tumors

    PubMed Central

    Yoo, Heon; Jung, Eugene; Gwak, Ho Shin; Shin, Sang Hoon

    2011-01-01

    Purpose Hemorrhagic metastatic brain tumors are not rare, but little is known about the surgical outcome following treatment. We conducted this study to determine the result of the surgical outcome of hemorrhagic metastatic brain tumors. Materials and Methods From July 2001 to December 2008, 21 patients underwent surgery for hemorrhagic metastatic brain tumors at our institution. 15 patients had lung cancer, 3 had hepatocellular carcinoma, and the rest had rectal cancer, renal cell carcinoma, and sarcoma. 20 patients had macroscopic hemorrhage in the tumors, and one patient had intracerebral hemorrhage surrounding the tumor. A retrospective clinical review was conducted focusing on the patterns of presenting symptoms and signs, as well as local recurrence following surgery. Results Among 21 hemorrhagic brain metastases, local recurrence developed in two patients. The 12 month progression free survival rate was 86.1%. Mean time to progression was 20.8 months and median survival time after surgery was 11.7 months. Conclusion The results of our study showed that hemorrhagic metastatic brain tumors rarely recurred after surgery. Surgery should be considered as a good treatment option for hemorrhagic brain metastasis, especially in cases with increased intracranial pressure or severe neurologic deficits. PMID:21811426

  7. Brain tumor immunotherapy: an immunologist's perspective.

    PubMed

    Lampson, Lois A

    2003-01-01

    Key concepts in brain tumor immunotherapy are reviewed. "Immunotherapy" can refer to a fully-developed, tumor-specific immune response, or to its individual cellular or molecular mediators. The immune response is initiated most efficiently in organized lymphoid tissue. After initiation, antigen-specific T lymphocytes (T cells) survey the tissues--including the brain. If the T cells re-encounter their antigen at a tumor site, they can be triggered to carry out their effector functions. T cells can attack tumor in many ways, directly and indirectly, through cell-cell contact, secreted factors, and attraction and activation of other cells, endogenous or blood-borne. Recent work expands the list of candidate tumor antigens: they are not limited to cell surface proteins and need not be absolutely tumor-specific. Once identified, tumor antigens can be targeted immunologically, or in novel ways. The immune response is under complex regulatory control. Most current work aims to enhance initiation of the response (for example, with tumor vaccines), rather than enhancing the effector phase at the tumor site. The effector phase includes a rich, interactive set of cells and mediators; some that are not usually stressed are of particular interest against tumor in the brain. Within the brain, immune regulation varies from site to site, and local neurochemicals (such as substance P or glutamate) can contribute to local control. Given the complexity of a tumor, the brain, and the immune response, animal models are essential, but more emphasis should be given to their limitations and to step-by-step analysis, rather than animal "cures".

  8. Oncogenic extracellular vesicles in brain tumor progression.

    PubMed

    D'Asti, Esterina; Garnier, Delphine; Lee, Tae H; Montermini, Laura; Meehan, Brian; Rak, Janusz

    2012-01-01

    The brain is a frequent site of neoplastic growth, including both primary and metastatic tumors. The clinical intractability of many brain tumors and their distinct biology are implicitly linked to the unique microenvironment of the central nervous system (CNS) and cellular interactions within. Among the most intriguing forms of cellular interactions is that mediated by membrane-derived extracellular vesicles (EVs). Their biogenesis (vesiculation) and uptake by recipient cells serves as a unique mechanism of intercellular trafficking of complex biological messages including the exchange of molecules that cannot be released through classical secretory pathways, or that are prone to extracellular degradation. Tumor cells produce EVs containing molecular effectors of several cancer-related processes such as growth, invasion, drug resistance, angiogenesis, and coagulopathy. Notably, tumor-derived EVs (oncosomes) also contain oncogenic proteins, transcripts, DNA, and microRNA (miR). Uptake of this material may change properties of the recipient cells and impact the tumor microenvironment. Examples of transformation-related molecules found in the cargo of tumor-derived EVs include the oncogenic epidermal growth factor receptor (EGFRvIII), tumor suppressors (PTEN), and oncomirs (miR-520g). It is postulated that EVs circulating in blood or cerebrospinal fluid (CSF) of brain tumor patients may be used to decipher molecular features (mutations) of the underlying malignancy, reflect responses to therapy, or molecular subtypes of primary brain tumors [e.g., glioma or medulloblastoma (MB)]. It is possible that metastases to the brain may also emit EVs with clinically relevant oncogenic signatures. Thus, EVs emerge as a novel and functionally important vehicle of intercellular communication that can mediate multiple biological effects. In addition, they provide a unique platform to develop molecular biomarkers in brain malignancies. PMID:22934045

  9. Fuzzy local Gaussian mixture model for brain MR image segmentation.

    PubMed

    Ji, Zexuan; Xia, Yong; Sun, Quansen; Chen, Qiang; Xia, Deshen; Feng, David Dagan

    2012-05-01

    Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.

  10. General Information about Childhood Brain and Spinal Cord Tumors

    MedlinePlus

    ... Cord Tumors Treatment Overview (PDQ®)–Patient Version General Information About Childhood Brain and Spinal Cord Tumors Go ... types of brain and spinal cord tumors. The information from tests and procedures done to detect (find) ...

  11. Proton MRS imaging in pediatric brain tumors.

    PubMed

    Zarifi, Maria; Tzika, A Aria

    2016-06-01

    Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors. PMID:27233788

  12. Fast and intuitive segmentation of gyri of the human brain

    NASA Astrophysics Data System (ADS)

    Weiler, Florian; Hahn, Horst K.

    2015-03-01

    The cortical surface of the human brain consists of a large number of folds forming valleys and ridges, the gyri and sulci. Often, it is desirable to perform a segmentation of a brain image into these underlying structures in order to assess parameters relative to these functional components. Typical examples for this include measurements of cortical thickness for individual functional areas, or the correlation of functional areas derived from fMRI data to corresponding anatomical areas seen in structural imaging. In this paper, we present a novel interactive technique, that allows for fast and intuitive segmentation of these functional areas from T1-weighted MR images of the brain. Our segmentation approach is based exclusively on morphological image processing operations, eliminating the requirement for explicit reconstruction of the brains surface.

  13. Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials.

    PubMed

    Cordova, James S; Schreibmann, Eduard; Hadjipanayis, Costas G; Guo, Ying; Shu, Hui-Kuo G; Shim, Hyunsuk; Holder, Chad A

    2014-02-01

    Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T 1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials. PMID:24772206

  14. Quantitative Tumor Segmentation for Evaluation of Extent of Glioblastoma Resection to Facilitate Multisite Clinical Trials12

    PubMed Central

    Cordova, James S; Schreibmann, Eduard; Hadjipanayis, Costas G; Guo, Ying; Shu, Hui-Kuo G; Shim, Hyunsuk; Holder, Chad A

    2014-01-01

    Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials. PMID:24772206

  15. [A unusual brain cortical tumor: angiocentric glioma].

    PubMed

    Tauziède-Espariat, Arnault; Fohlen, Martine; Ferrand-Sorbets, Sarah; Polivka, Marc

    2015-04-01

    We report the case of an 11-year-old girl, who was admitted for surgery of an epilepsy-associated brain tumor. The radiological and clinical hypothesis was dysembryoplasic neuroepithelial tumor. Histopathological examination revealed a tumoral proliferation composed of spindle-shaped cells with palisade arrangements around vessels. Tumor cells have small, round and regular nuclei without atypia or mitosis. On immunohistochemistry, the neoplastic cells strongly expressed GFAP and showed a characteristic cytoplasmic dot-like staining with EMA (epithelial membrane antigen). Ki-67 labeling index was low. Molecular analysis failed to reveal the V600E mutation of BRAF gene. The patient was free of seizures after surgery. Angiocentric glioma is a rare brain tumor occuring preferably in children and young adults and is associated with seizures. The precise histogenesis remains debated. The treatment of choice is total resection. The prognosis is favorable if totally resected.

  16. Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

    PubMed Central

    Angelini, Elsa D.; Song, Ting; Mensh, Brett D.; Laine, Andrew F.

    2007-01-01

    This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method. PMID:18253474

  17. Multiscale modeling for image analysis of brain tumor studies.

    PubMed

    Bauer, Stefan; May, Christian; Dionysiou, Dimitra; Stamatakos, Georgios; Büchler, Philippe; Reyes, Mauricio

    2012-01-01

    Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlas-based segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression. PMID:21813362

  18. MRI and MRS of human brain tumors.

    PubMed

    Hou, Bob L; Hu, Jiani

    2009-01-01

    The purpose of this chapter is to provide an introduction to magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) of human brain tumors, including the primary applications and basic terminology involved. Readers who wish to know more about this broad subject should seek out the referenced books (1. Tofts (2003) Quantitative MRI of the brain. Measuring changes caused by disease. Wiley; Bradley and Stark (1999) 2. Magnetic resonance imaging, 3rd Edition. Mosby Inc; Brown and Semelka (2003) 3. MRI basic principles and applications, 3rd Edition. Wiley-Liss) or reviews (4. Top Magn Reson Imaging 17:127-36, 2006; 5. JMRI 24:709-724, 2006; 6. Am J Neuroradiol 27:1404-1411, 2006).MRI is the most popular means of diagnosing human brain tumors. The inherent difference in the magnetic resonance (MR) properties of water between normal tissues and tumors results in contrast differences on the image that provide the basis for distinguishing tumors from normal tissues. In contrast to MRI, which provides spatial maps or images using water signals of the tissues, proton MRS detects signals of tissue metabolites. MRS can complement MRI because the observed MRS peaks can be linked to inherent differences in biochemical profiles between normal tissues and tumors.The goal of MRI and MRS is to characterize brain tumors, including tumor core, edge, edema, volume, types, and grade. The commonly used brain tumor MRI protocol includes T2-weighted images and T1-weighted images taken both before and after the injection of a contrast agent (typically gadolinium: Gd). The commonly used MRS technique is either point-resolved spectroscopy (PRESS) or stimulated echo acquisition mode (STEAM).

  19. Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain.

    PubMed

    Verma, Nishant; Muralidhar, Gautam S; Bovik, Alan C; Cowperthwaite, Matthew C; Markey, Mia K

    2011-01-01

    Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional region-based formulations model local region intensity by assuming a piecewise constant approximation. However, the piecewise constant approximation rarely holds true for medical images such as MR images due to the presence of noise and bias field, which invariably results in a poor segmentation of the image. To overcome this problem, we have developed a probabilistic region-based active contour model for automatic segmentation of MR images of the brain. In our approach, a mixture of Gaussian distributions is used to accurately model the arbitrarily shaped local region intensity distribution. Prior spatial information derived from probabilistic atlases is also integrated into the level set evolution framework for guiding the segmentation process. Our experiments with a series of publicly available brain MR images show that the proposed active contour model gives stable and accurate segmentation results when compared to the traditional region based formulations. PMID:22254928

  20. Fuzzy object models for newborn brain MR image segmentation

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Udupa, Jayaram K.

    2013-03-01

    Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

  1. Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation

    PubMed Central

    Linguraru, Marius George; Richbourg, William J.; Liu, Jianfei; Watt, Jeremy M.; Pamulapati, Vivek; Wang, Shijun; Summers, Ronald M.

    2013-01-01

    The paper presents the automated computation of hepatic tumor burden from abdominal CT images of diseased populations with images with inconsistent enhancement. The automated segmentation of livers is addressed first. A novel three-dimensional (3D) affine invariant shape parameterization is employed to compare local shape across organs. By generating a regular sampling of the organ's surface, this parameterization can be effectively used to compare features of a set of closed 3D surfaces point-to-point, while avoiding common problems with the parameterization of concave surfaces. From an initial segmentation of the livers, the areas of atypical local shape are determined using training sets. A geodesic active contour corrects locally the segmentations of the livers in abnormal images. Graph cuts segment the hepatic tumors using shape and enhancement constraints. Liver segmentation errors are reduced significantly and all tumors are detected. Finally, support vector machines and feature selection are employed to reduce the number of false tumor detections. The tumor detection true position fraction of 100% is achieved at 2.3 false positives/case and the tumor burden is estimated with 0.9% error. Results from the test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow the temporal monitoring of patients with hepatic cancer. PMID:22893379

  2. Metabolic brain imaging correlated with clinical features of brain tumors

    SciTech Connect

    Alavi, J.; Alavi, A.; Dann, R.; Kushner, M.; Chawluk, J.; Powlis, W.; Reivich, M.

    1985-05-01

    Nineteen adults with brain tumors have been studied with positron emission tomography utilizing FDG. Fourteen had biopsy proven cerebral malignant glioma, one each had meningioma, hemangiopericytoma, primitive neuroectodermal tumor (PNET), two had unbiopsied lesions, and one patient had an area of biopsy proven radiation necrosis. Three different patterns of glucose metabolism are observed: marked increase in metabolism at the site of the known tumor in (10 high grade gliomas and the PNET), lower than normal metabolism at the tumor (in 1 grade II glioma, 3 grade III gliomas, 2 unbiopsied low density nonenhancing lesions, and the meningioma), no abnormality (1 enhancing glioma, the hemangiopericytoma and the radiation necrosis.) The metabolic rate of the tumor or the surrounding brain did not appear to be correlated with the history of previous irradiation or chemotherapy. Decreased metabolism was frequently observed in the rest of the affected hemisphere and in the contralateral cerebellum. Tumors of high grade or with enhancing CT characteristics were more likely to show increased metabolism. Among the patients with proven gliomas, survival after PETT scan tended to be longer for those with low metabolic activity tumors than for those with highly active tumors. The authors conclude that PETT may help to predict the malignant potential of tumors, and may add useful clinical information to the CT scan.

  3. Psychiatric aspects of brain tumors: A review

    PubMed Central

    Madhusoodanan, Subramoniam; Ting, Mark Bryan; Farah, Tara; Ugur, Umran

    2015-01-01

    Infrequently, psychiatric symptoms may be the only manifestation of brain tumors. They may present with mood symptoms, psychosis, memory problems, personality changes, anxiety, or anorexia. Symptoms may be misleading, complicating the clinical picture. A comprehensive review of the literature was conducted regarding reports of brain tumors and psychiatric symptoms from 1956-2014. Search engines used include PubMed, Ovid, Psych Info, MEDLINE, and MedScape. Search terms included psychiatric manifestations/symptoms, brain tumors/neoplasms. Our literature search yielded case reports, case studies, and case series. There are no double blind studies except for post-diagnosis/-surgery studies. Early diagnosis is critical for improved quality of life. Symptoms that suggest work-up with neuroimaging include: new-onset psychosis, mood/memory symptoms, occurrence of new or atypical symptoms, personality changes, and anorexia without body dysmorphic symptoms. This article reviews the existing literature regarding the diagnosis and management of this clinically complex condition. PMID:26425442

  4. Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.

    PubMed

    Hu, Yu-Chi; Grossberg, Michael; Mageras, Gikas

    2016-04-01

    Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance. PMID:27413768

  5. BEaST: brain extraction based on nonlocal segmentation technique.

    PubMed

    Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V; Leung, Kelvin K; Guizard, Nicolas; Wassef, Shafik N; Østergaard, Lasse Riis; Collins, D Louis

    2012-02-01

    Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

  6. NABS: non-local automatic brain hemisphere segmentation.

    PubMed

    Romero, José E; Manjón, José V; Tohka, Jussi; Coupé, Pierrick; Robles, Montserrat

    2015-05-01

    In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease. PMID:25660644

  7. Possibilistic-clustering-based MR brain image segmentation with accurate initialization

    NASA Astrophysics Data System (ADS)

    Liao, Qingmin; Deng, Yingying; Dou, Weibei; Ruan, Su; Bloyet, Daniel

    2004-01-01

    Magnetic resonance image analysis by computer is useful to aid diagnosis of malady. We present in this paper a automatic segmentation method for principal brain tissues. It is based on the possibilistic clustering approach, which is an improved fuzzy c-means clustering method. In order to improve the efficiency of clustering process, the initial value problem is discussed and solved by combining with a histogram analysis method. Our method can automatically determine number of classes to cluster and the initial values for each class. It has been tested on a set of forty MR brain images with or without the presence of tumor. The experimental results showed that it is simple, rapid and robust to segment the principal brain tissues.

  8. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation.

    PubMed

    Beare, Richard J; Chen, Jian; Kelly, Claire E; Alexopoulos, Dimitrios; Smyser, Christopher D; Rogers, Cynthia E; Loh, Wai Y; Matthews, Lillian G; Cheong, Jeanie L Y; Spittle, Alicia J; Anderson, Peter J; Doyle, Lex W; Inder, Terrie E; Seal, Marc L; Thompson, Deanne K

    2016-01-01

    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T 2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T 2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical

  9. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    PubMed Central

    Beare, Richard J.; Chen, Jian; Kelly, Claire E.; Alexopoulos, Dimitrios; Smyser, Christopher D.; Rogers, Cynthia E.; Loh, Wai Y.; Matthews, Lillian G.; Cheong, Jeanie L. Y.; Spittle, Alicia J.; Anderson, Peter J.; Doyle, Lex W.; Inder, Terrie E.; Seal, Marc L.; Thompson, Deanne K.

    2016-01-01

    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray

  10. Head, neck, and brain tumor embolization guidelines

    PubMed Central

    Duffis, E Jesus; Prestigiacomo, Charles Joseph; Abruzzo, Todd; Albuquerque, Felipe; Bulsara, Ketan R; Derdeyn, Colin P; Fraser, Justin F; Hirsch, Joshua A; Hussain, Muhammad Shazam; Do, Huy M; Jayaraman, Mahesh V; Meyers, Philip M; Narayanan, Sandra

    2012-01-01

    Background Management of vascular tumors of the head, neck, and brain is often complex and requires a multidisciplinary approach. Peri-operative embolization of vascular tumors may help to reduce intra-operative bleeding and operative times and have thus become an integral part of the management of these tumors. Advances in catheter and non-catheter based techniques in conjunction with the growing field of neurointerventional surgery is likely to expand the number of peri-operative embolizations performed. The goal of this article is to provide consensus reporting standards and guidelines for embolization treatment of vascular head, neck, and brain tumors. Summary This article was produced by a writing group comprised of members of the Society of Neurointerventional Surgery. A computerized literature search using the National Library of Medicine database (Pubmed) was conducted for relevant articles published between 1 January 1990 and 31 December 2010. The article summarizes the effectiveness and safety of peri-operative vascular tumor embolization. In addition, this document provides consensus definitions and reporting standards as well as guidelines not intended to represent the standard of care, but rather to provide uniformity in subsequent trials and studies involving embolization of vascular head and neck as well as brain tumors. Conclusions Peri-operative embolization of vascular head, neck, and brain tumors is an effective and safe adjuvant to surgical resection. Major complications reported in the literature are rare when these procedures are performed by operators with appropriate training and knowledge of the relevant vascular and surgical anatomy. These standards may help to standardize reporting and publication in future studies. PMID:22539531

  11. [Conformal radiotherapy of brain tumors].

    PubMed

    Haie-Meder, C; Beaudré, A; Breton, C; Biron, B; Cordova, A; Dubray, B; Mazeron, J J

    1999-01-01

    Conformal irradiation of brain tumours is based on the three-dimensional reconstruction of the targeted volumes and at-risk organ images, the three-dimensional calculation of the dose distribution and a treatment device (immobilisation, beam energy, collimation, etc.) adapted to the high precision required by the procedure. Each step requires an appropriate methodology and a quality insurance program. Specific difficulties in brain tumour management are related to GTV and CTV definition depending upon the histological type, the quality of the surgical resection and the medical team. Clinical studies have reported dose escalation trials, mostly in high-grade gliomas and tumours at the base of the skull. Clinical data are now providing a better knowledge of the tolerance of normal tissues. As for small tumours, the implementation of beam intensity modulation is likely to narrow the gap between conformal and stereotaxic radiotherapy. PMID:10572510

  12. The delivery of BCNU to brain tumors.

    PubMed

    Wang, C C; Li, J; Teo, C S; Lee, T

    1999-08-27

    This paper reports the development of three-dimensional simulations to study the effect of various factors on the delivery of 1-3-bis(2-chloroethyl)-1-nitrosourea (BCNU) to brain tumors. The study yields information on the efficacy of various delivery methods, and the optimal location of polymer implantation. Two types of drug deliveries, namely, systemic administration and controlled release from polymers, were simulated using fluid dynamics analysis package (FIDAP) to predict the temporal and spatial variation of drug distribution. Polymer-based delivery provides higher mean concentration, longer BCNU exposure time and reduced systemic toxicity than bolus injection. Polymer implanted in the core gives higher concentration of drug in both the core and viable zone than the polymer in the viable zone case. The penetration depth of BCNU is very short. This is because BCNU can get drained out of the system before diffusing to any appreciable distance. Since transvascular permeation is the dominant means of BCNU delivery, the interstitial convection has minor effect because of the extremely small transvascular Peclet number. The reaction of BCNU with brain tissues reduces the drug concentration in all regions and its effect increases with rate constant. The implantation of BCNU/ethylene-vinyl acetate copolymer (EVAc) matrix at the lumen of the viable zone immediately following the surgical removal of 80% of the tumor may be an effective treatment for the chemotherapy of brain tumors. The present study provides a quantitative examination on the working principle of Gliadel wafer for the treatment of brain tumors.

  13. [SURGICAL TREATMENT OF TUMORS OF THE LEFT PANCREATIC ANATOMICAL SEGMENT].

    PubMed

    Kopchak, V M; Tkachuk, O S; Kopchak, K V; Duvalko, O V; Khomyak, I V; Pererva, L O; Kvasivka, O O; Andronik, S V; Shevkolenko, G G; Khanenko, V V; Romaniv, Ya V; Grebihn, R M

    2015-04-01

    The results of treatment of 231 patients, suffering tumoral affection of pancreatic left anatomical segment in period of 2009-2013 yrs were analyzed. Individualized approach, using modern technologies, was applied. Radical operations were performed in 129 patients, ageing 14-81 yrs old, including pancreatic distal resections in various modifications, central resection and tumoral enucleation. Possibilities of the extended pancreatic resection performance were studied in conditions of tumoral invasion of adjacent organs, regional vessels, as well as impact of such interventions on postoperative complications and lethality rate. While performing pancreatic subtotal distal resection with simultant resection of affected main venous vessels and adjacent organs the operative intervention risk is enhanced, but possibilities of a radical operations performance in previously considered inoperable patients are expanding.

  14. Magnetic resonance brain tissue segmentation based on sparse representations

    NASA Astrophysics Data System (ADS)

    Rueda, Andrea

    2015-12-01

    Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

  15. Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

    PubMed

    Cuadra, Meritxell Bach; Pollo, Claudio; Bardera, Anton; Cuisenaire, Olivier; Villemure, Jean-Guy; Thiran, Jean-Philippe

    2004-10-01

    We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy. PMID:15493697

  16. Segmentation of confocal microscopic image of insect brain

    NASA Astrophysics Data System (ADS)

    Wu, Ming-Jin; Lin, Chih-Yang; Ching, Yu-Tai

    2002-05-01

    Accurate analysis of insect brain structures in digital confocal microscopic images is valuable and important to biology research needs. The first step is to segment meaningful structures from images. Active contour model, known as snakes, is widely used for segmentation of medical images. A new class of active contour model called gradient vector flow snake has been introduced in 1998 to overcome some critical problems encountered in the traditional snake. In this paper, we use gradient vector flow snake to segment the mushroom body and the central body from the confocal microscopic insect brain images. First, an edge map is created from images by some edge filters. Second, a gradient vector flow field is calculated from the edge map using a computational diffusion process. Finally, a traditional snake deformation process starts until it reaches a stable configuration. User interface is also provided here, allowing users to edit the snake during deformation process, if desired. Using the gradient vector flow snake as the main segmentation method and assist with user interface, we can properly segment the confocal microscopic insect brain image for most of the cases. The identified mushroom and central body can then be used as the preliminary results toward a 3-D reconstruction process for further biology researches.

  17. [Differential infratentorial brain tumor diagnosis in children].

    PubMed

    Warmuth-Metz, M; Kühl, J; Rutkowski, S; Krauss, J; Solymosi, L

    2003-11-01

    With the exception of the first year of life, infratentorial brain tumors are more frequent in the first decade than tumors in the supratentorial compartment. In particular these are cerebellar low-grade astrocytomas, medulloblastomas, brainstem gliomas and ependymomas of the fourth ventricle. The morphology on MRI and CT and the mode of dissemination permit differential diagnosis in many cases. To allow correct stratification into different treatments in possibly disseminating malignant brain tumors, knowledge of the status of dissemination is essential, and therefore not only cranial but also spinal MRI is indispensable for staging. If the spinal MRI is performed in the immediate postoperative period, knowledge of the normal non-specific purely postoperative changes, often seen as enhancement in the subdural spinal spaces, is necessary in order to avoid misinterpretation as meningial seeding. The differential diagnosis of pediatric infratentorial brain tumors and the morphology of subdural enhancement are illustrated with typical images. The natural history of the most frequent tumors and its importance for treatment decisions is discussed in light of the literature.

  18. A Rare Malignant Fetal Brain Tumor.

    PubMed

    Iruretagoyena, Jesus Igor; Heiser, Timothy; Iskandar, Bermans; Shah, Dinesh

    2016-01-01

    A gravida 4, para 3 female at 37 weeks' gestation presented for a routine ultrasound. She had an otherwise uncomplicated low-risk pregnancy. The sonographic evaluation of the fetus revealed a macrocephaly and a deviation of the brain midline structures with a mass effect as well as a massively dilated left cerebral ventricular system with ill-defined echogenic ventricular delineation. Multiple free intracavitary echogenicities and disruptions of the brain mantle were visible. Our images were suggestive of either an intracranial bleed with the presence of an underlying tumor or a spontaneous bleed. A postnatal MRI was consistent with our prenatal findings of a possible tumor. The postnatal biopsy revealed an anaplastic astroblastoma within a hemorrhagic background. The infant received multiple courses of chemotherapy and further tumor debulking. At present, the infant is 18 months old. This is only the 4th case of an astrocytoma identified in the fetal period, and our case has the longest known survival yet. PMID:26044034

  19. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    NASA Technical Reports Server (NTRS)

    Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

    1992-01-01

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

  20. Brain structure resolves the segmental affinity of anomalocaridid appendages.

    PubMed

    Cong, Peiyun; Ma, Xiaoya; Hou, Xianguang; Edgecombe, Gregory D; Strausfeld, Nicholas J

    2014-09-25

    Despite being among the most celebrated taxa from Cambrian biotas, anomalocaridids (order Radiodonta) have provoked intense debate about their affinities within the moulting-animal clade that includes Arthropoda. Current alternatives identify anomalocaridids as either stem-group euarthropods, crown-group euarthropods near the ancestry of chelicerates, or a segmented ecdysozoan lineage with convergent similarity to arthropods in appendage construction. Determining unambiguous affinities has been impeded by uncertainties about the segmental affiliation of anomalocaridid frontal appendages. These structures are variably homologized with jointed appendages of the second (deutocerebral) head segment, including antennae and 'great appendages' of Cambrian arthropods, or with the paired antenniform frontal appendages of living Onychophora and some Cambrian lobopodians. Here we describe Lyrarapax unguispinus, a new anomalocaridid from the early Cambrian Chengjiang biota, southwest China, nearly complete specimens of which preserve traces of muscles, digestive tract and brain. The traces of brain provide the first direct evidence for the segmental composition of the anomalocaridid head and its appendicular organization. Carbon-rich areas in the head resolve paired pre-protocerebral ganglia at the origin of paired frontal appendages. The ganglia connect to areas indicative of a bilateral pre-oral brain that receives projections from the eyestalk neuropils and compound retina. The dorsal, segmented brain of L. unguispinus reinforces an alliance between anomalocaridids and arthropods rather than cycloneuralians. Correspondences in brain organization between anomalocaridids and Onychophora resolve pre-protocerebral ganglia, associated with pre-ocular frontal appendages, as characters of the last common ancestor of euarthropods and onychophorans. A position of Radiodonta on the euarthropod stem-lineage implies the transformation of frontal appendages to another structure in crown

  1. Brain structure resolves the segmental affinity of anomalocaridid appendages.

    PubMed

    Cong, Peiyun; Ma, Xiaoya; Hou, Xianguang; Edgecombe, Gregory D; Strausfeld, Nicholas J

    2014-09-25

    Despite being among the most celebrated taxa from Cambrian biotas, anomalocaridids (order Radiodonta) have provoked intense debate about their affinities within the moulting-animal clade that includes Arthropoda. Current alternatives identify anomalocaridids as either stem-group euarthropods, crown-group euarthropods near the ancestry of chelicerates, or a segmented ecdysozoan lineage with convergent similarity to arthropods in appendage construction. Determining unambiguous affinities has been impeded by uncertainties about the segmental affiliation of anomalocaridid frontal appendages. These structures are variably homologized with jointed appendages of the second (deutocerebral) head segment, including antennae and 'great appendages' of Cambrian arthropods, or with the paired antenniform frontal appendages of living Onychophora and some Cambrian lobopodians. Here we describe Lyrarapax unguispinus, a new anomalocaridid from the early Cambrian Chengjiang biota, southwest China, nearly complete specimens of which preserve traces of muscles, digestive tract and brain. The traces of brain provide the first direct evidence for the segmental composition of the anomalocaridid head and its appendicular organization. Carbon-rich areas in the head resolve paired pre-protocerebral ganglia at the origin of paired frontal appendages. The ganglia connect to areas indicative of a bilateral pre-oral brain that receives projections from the eyestalk neuropils and compound retina. The dorsal, segmented brain of L. unguispinus reinforces an alliance between anomalocaridids and arthropods rather than cycloneuralians. Correspondences in brain organization between anomalocaridids and Onychophora resolve pre-protocerebral ganglia, associated with pre-ocular frontal appendages, as characters of the last common ancestor of euarthropods and onychophorans. A position of Radiodonta on the euarthropod stem-lineage implies the transformation of frontal appendages to another structure in crown

  2. A survey of MRI-based medical image analysis for brain tumor studies.

    PubMed

    Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P; Reyes, Mauricio

    2013-07-01

    MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines. PMID:23743802

  3. Asymmetric bias in user guided segmentations of brain structures.

    PubMed

    Maltbie, Eric; Bhatt, Kshamta; Paniagua, Beatriz; Smith, Rachel G; Graves, Michael M; Mosconi, Matthew W; Peterson, Sarah; White, Scott; Blocher, Joseph; El-Sayed, Mohammed; Hazlett, Heather C; Styner, Martin A

    2012-01-16

    Brain morphometric studies often incorporate comparative hemispheric asymmetry analyses of segmented brain structures. In this work, we present evidence that common user guided structural segmentation techniques exhibit strong left-right asymmetric biases and thus fundamentally influence any left-right asymmetry analyses. In this study, MRI scans from ten pediatric subjects were employed for studying segmentations of amygdala, globus pallidus, putamen, caudate, and lateral ventricle. Additionally, two pediatric and three adult scans were used for studying hippocampus segmentation. Segmentations of the sub-cortical structures were performed by skilled raters using standard manual and semi-automated methods. The left-right mirrored versions of each image were included in the data and segmented in a random order to assess potential left-right asymmetric bias. Using shape analysis we further assessed whether the asymmetric bias is consistent across subjects and raters with the focus on the hippocampus. The user guided segmentation techniques on the sub-cortical structures exhibited left-right asymmetric volume bias with the hippocampus displaying the most significant asymmetry values (p<0.01). The hippocampal shape analysis revealed the bias to be strongest on the lateral side of the body and medial side of the head and tail. The origin of this asymmetric bias is considered to be based in laterality of visual perception; therefore segmentations with any degree of user interaction contain an asymmetric bias. The aim of our study is to raise awareness in the neuroimaging community regarding the presence of the asymmetric bias and its influence on any left-right hemispheric analyses. We also recommend reexamining previous research results in the light of this new finding. PMID:21889995

  4. Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.

    PubMed

    Parisot, Sarah; Wells, William; Chemouny, Stéphane; Duffau, Hugues; Paragios, Nikos

    2014-05-01

    In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. PMID:24717540

  5. Brain segmentation and the generation of cortical surfaces

    NASA Technical Reports Server (NTRS)

    Joshi, M.; Cui, J.; Doolittle, K.; Joshi, S.; Van Essen, D.; Wang, L.; Miller, M. I.

    1999-01-01

    This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

  6. Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images.

    PubMed

    Hamoud Al-Tamimi, Mohammed Sabbih; Sulong, Ghazali; Shuaib, Ibrahim Lutfi

    2015-07-01

    Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor. PMID:25865822

  7. Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images.

    PubMed

    Hamoud Al-Tamimi, Mohammed Sabbih; Sulong, Ghazali; Shuaib, Ibrahim Lutfi

    2015-07-01

    Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.

  8. Brain blood vessel segmentation using line-shaped profiles

    NASA Astrophysics Data System (ADS)

    Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

    2013-11-01

    Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

  9. Automatic segmentation of MR brain images in multiple sclerosis patients

    NASA Astrophysics Data System (ADS)

    Avula, Ramesh T. V.; Erickson, Bradley J.

    1996-04-01

    A totally automatic scheme for segmenting brain from extracranial tissues and to classify all intracranial voxels as CSF, gray matter (GM), white matter (WM), or abnormality such as multiple sclerosis (MS) lesions is presented in this paper. It is observed that in MR head images, if a tissue's intensity values are normalized, its relationship to the other tissues is essentially constant for a given type of image. Based on this approach, the subcutaneous fat surrounding the head is normalized to classify other tissues. Spatially registered 3 mm MR head image slices of T1 weighted, fast spin echo [dual echo T2 weighted and proton density (PD) weighted images] and fast fluid attenuated inversion recovery (FLAIR) sequences are used for segmentation. Subcutaneous fat surrounding the skull was identified based on intensity thresholding from T1 weighted images. A multiparametric space map was developed for CSF, GM and WM by normalizing each tissue with respect to the mean value of corresponding subcutaneous fat on each pulse sequence. To reduce the low frequency noise without blurring the fine morphological high frequency details an anisotropic diffusion filter was applied to all images before segmentation. An initial slice by slice classification was followed by morphological operations to delete any brides connecting extracranial segments. Finally 3-dimensional region growing of the segmented brain extracts GM, WM and pathology. The algorithm was tested on sequential scans of 10 patients with MS lesions. For well registered sequences, tissues and pathology have been accurately classified. This procedure does not require user input or image training data sets, and shows promise for automatic classification of brain and pathology.

  10. Brain tissue segmentation in 4D CT using voxel classification

    NASA Astrophysics Data System (ADS)

    van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

    2012-02-01

    A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

  11. Quantifying brain development in early childhood using segmentation and registration

    NASA Astrophysics Data System (ADS)

    Aljabar, P.; Bhatia, K. K.; Murgasova, M.; Hajnal, J. V.; Boardman, J. P.; Srinivasan, L.; Rutherford, M. A.; Dyet, L. E.; Edwards, A. D.; Rueckert, D.

    2007-03-01

    In this work we obtain estimates of tissue growth using longitudinal data comprising MR brain images of 25 preterm children scanned at one and two years. The growth estimates are obtained using segmentation and registration based methods. The segmentation approach used an expectation maximisation (EM) method to classify tissue types and the registration approach used tensor based morphometry (TBM) applied to a free form deformation (FFD) model. The two methods show very good agreement indicating that the registration and segmentation approaches can be used interchangeably. The advantage of the registration based method, however, is that it can provide more local estimates of tissue growth. This is the first longitudinal study of growth in early childhood, previous longitudinal studies have focused on later periods during childhood.

  12. Quantitative evaluation of six graph based semi-automatic liver tumor segmentation techniques using multiple sets of reference segmentation

    NASA Astrophysics Data System (ADS)

    Su, Zihua; Deng, Xiang; Chefd'hotel, Christophe; Grady, Leo; Fei, Jun; Zheng, Dong; Chen, Ning; Xu, Xiaodong

    2011-03-01

    Graph based semi-automatic tumor segmentation techniques have demonstrated great potential in efficiently measuring tumor size from CT images. Comprehensive and quantitative validation is essential to ensure the efficacy of graph based tumor segmentation techniques in clinical applications. In this paper, we present a quantitative validation study of six graph based 3D semi-automatic tumor segmentation techniques using multiple sets of expert segmentation. The six segmentation techniques are Random Walk (RW), Watershed based Random Walk (WRW), LazySnapping (LS), GraphCut (GHC), GrabCut (GBC), and GrowCut (GWC) algorithms. The validation was conducted using clinical CT data of 29 liver tumors and four sets of expert segmentation. The performance of the six algorithms was evaluated using accuracy and reproducibility. The accuracy was quantified using Normalized Probabilistic Rand Index (NPRI), which takes into account of the variation of multiple expert segmentations. The reproducibility was evaluated by the change of the NPRI from 10 different sets of user initializations. Our results from the accuracy test demonstrated that RW (0.63) showed the highest NPRI value, compared to WRW (0.61), GWC (0.60), GHC (0.58), LS (0.57), GBC (0.27). The results from the reproducibility test indicated that GBC is more sensitive to user initialization than the other five algorithms. Compared to previous tumor segmentation validation studies using one set of reference segmentation, our evaluation methods use multiple sets of expert segmentation to address the inter or intra rater variability issue in ground truth annotation, and provide quantitative assessment for comparing different segmentation algorithms.

  13. Registration, segmentation, and visualization of multimodal brain images.

    PubMed

    Viergever, M A; Maintz, J B; Niessen, W J; Noordmans, H J; Pluim, J P; Stokking, R; Vincken, K L

    2001-01-01

    This paper gives an overview of the studies performed at our institute over the last decade on the processing and visualization of brain images, in the context of international developments in the field. The focus is on multimodal image registration and multimodal visualization, while segmentation is touched upon as a preprocessing step for visualization. The state-of-the-art in these areas is discussed and suggestions for future research are given. PMID:11137791

  14. Subacute brain atrophy after radiation therapy for malignant brain tumor

    SciTech Connect

    Asai, A.; Matsutani, M.; Kohno, T.; Nakamura, O.; Tanaka, H.; Fujimaki, T.; Funada, N.; Matsuda, T.; Nagata, K.; Takakura, K.

    1989-05-15

    Brain atrophy with mental and neurologic deterioration developing a few months after radiation therapy in patients without residual or recurrent brain tumors has been recognized. Two illustrative case reports of this pathologic entity are presented. Six autopsy cases with this entity including the two cases were reviewed neurologically, radiographically, and histopathologically. All patients presented progressive disturbances of mental status and consciousness, akinesia, and tremor-like involuntary movement. Computerized tomography (CT) demonstrated marked enlargement of the ventricles, moderate widening of the cortical sulci, and a moderately attenuated CT number for the white matter in all six patients. Four of the six patients had CSF drainage (ventriculoperitoneal shunt or continuous lumbar drainage), however, none of them improved. Histologic examination demonstrated swelling and loss of the myelin sheath in the white matter in all patients, and reactive astrocytosis in three of the six patients. Neither prominent neuronal loss in the cerebral cortex or basal ganglia, nor axonal loss in the white matter was generally identified. The blood vessels of the cerebral cortex and white matter were normal. Ependymal layer and the surrounding brain tissue were normal in all patients. These findings suggested that this pathologic condition results from demyelination secondary to direct neurotoxic effect of irradiation. The authors' previous report was reviewed and the differential diagnoses, the risk factors for this pathologic entity, and the indication for radiation therapy in aged patients with a malignant brain tumor are discussed.

  15. Brain Tumor Database, a free relational database for collection and analysis of brain tumor patient information.

    PubMed

    Bergamino, Maurizio; Hamilton, David J; Castelletti, Lara; Barletta, Laura; Castellan, Lucio

    2015-03-01

    In this study, we describe the development and utilization of a relational database designed to manage the clinical and radiological data of patients with brain tumors. The Brain Tumor Database was implemented using MySQL v.5.0, while the graphical user interface was created using PHP and HTML, thus making it easily accessible through a web browser. This web-based approach allows for multiple institutions to potentially access the database. The BT Database can record brain tumor patient information (e.g. clinical features, anatomical attributes, and radiological characteristics) and be used for clinical and research purposes. Analytic tools to automatically generate statistics and different plots are provided. The BT Database is a free and powerful user-friendly tool with a wide range of possible clinical and research applications in neurology and neurosurgery. The BT Database graphical user interface source code and manual are freely available at http://tumorsdatabase.altervista.org.

  16. Primary brain tumors, neural stem cell, and brain tumor cancer cells: where is the link?

    PubMed Central

    Germano, Isabelle; Swiss, Victoria; Casaccia, Patrizia

    2010-01-01

    The discovery of brain tumor-derived cells (BTSC) with the properties of stem cells has led to the formulation of the hypothesis that neural stem cells could be the cell of origin of primary brain tumors (PBT). In this review we present the most common molecular changes in PBT, define the criteria of identification of BTSC and discuss the similarities between the characteristics of these cells and those of the endogenous population of neural stem cells (NPCs) residing in germinal areas of the adult brain. Finally, we propose possible mechanisms of cancer initiation and progression and suggest a model of tumor initiation that includes intrinsic changes of resident NSC and potential changes in the microenvironment defining the niche where the NSC reside. PMID:20045420

  17. Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

    PubMed

    Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

    2016-07-01

    We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI. PMID:27417984

  18. Quick detection of brain tumors and edemas: a bounding box method using symmetry.

    PubMed

    Saha, Baidya Nath; Ray, Nilanjan; Greiner, Russell; Murtha, Albert; Zhang, Hong

    2012-03-01

    A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR modalities remains a challenging, computationally intensive task. In this paper, we propose a novel automated, fast, and approximate segmentation technique. The input is a patient study consisting of a set of MR slices, and its output is a subset of the slices that include axis-parallel boxes that circumscribe the tumors. Our approach is based on an unsupervised change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms. We prove that this score function admits a very fast (linear in image height and width) search to locate the bounding box. The average dice coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52, respectively, which significantly exceeds the scores for two other competitive region-based bounding box techniques. PMID:21719256

  19. Stereotactic Radiosurgery in Treating Patients With Brain Tumors

    ClinicalTrials.gov

    2012-03-21

    Adult Central Nervous System Germ Cell Tumor; Adult Malignant Meningioma; Adult Medulloblastoma; Adult Noninfiltrating Astrocytoma; Adult Oligodendroglioma; Adult Craniopharyngioma; Adult Meningioma; Brain Metastases; Adult Ependymoma; Adult Pineal Parenchymal Tumor; Adult Brain Stem Glioma; Adult Infiltrating Astrocytoma; Mixed Gliomas; Stage IV Peripheral Primitive Neuroectodermal Tumor

  20. Photodynamic Therapy for Malignant Brain Tumors

    PubMed Central

    AKIMOTO, Jiro

    2016-01-01

    Photodynamic therapy (PDT) using talaporfin sodium together with a semiconductor laser was approved in Japan in October 2003 as a less invasive therapy for early-stage lung cancer. The author believes that the principle of PDT would be applicable for controlling the invading front of malignant brain tumors and verified its efficacy through experiments using glioma cell lines and glioma xenograft models. An investigator-initiated clinical study was jointly conducted with Tokyo Women’s Medical University with the support of the Japan Medical Association. Patient enrollment was started in May 2009 and a total of 27 patients were enrolled by March 2012. Of 22 patients included in efficacy analysis, 13 patients with newly diagnosed glioblastoma showed progression-free survival of 12 months, progression-free survival at the site of laser irradiation of 20 months, 1-year survival of 100%, and overall survival of 24.8 months. In addition, the safety analysis of the 27 patients showed that adverse events directly related to PDT were mild. PDT was approved in Japan for health insurance coverage as a new intraoperative therapy with the indication for malignant brain tumors in September 2013. Currently, the post-marketing investigation in the accumulated patients has been conducted, and the preparation of guidelines, holding training courses, and dissemination of information on the safe implementation of PDT using web sites and videos, have been promoted. PDT is expected to be a breakthrough for the treatment of malignant glioma as a tumor cell-selective less invasive therapy for the infiltrated functional brain area. PMID:26888042

  1. Photodynamic Therapy for Malignant Brain Tumors.

    PubMed

    Akimoto, Jiro

    2016-01-01

    Photodynamic therapy (PDT) using talaporfin sodium together with a semiconductor laser was approved in Japan in October 2003 as a less invasive therapy for early-stage lung cancer. The author believes that the principle of PDT would be applicable for controlling the invading front of malignant brain tumors and verified its efficacy through experiments using glioma cell lines and glioma xenograft models. An investigator-initiated clinical study was jointly conducted with Tokyo Women's Medical University with the support of the Japan Medical Association. Patient enrollment was started in May 2009 and a total of 27 patients were enrolled by March 2012. Of 22 patients included in efficacy analysis, 13 patients with newly diagnosed glioblastoma showed progression-free survival of 12 months, progression-free survival at the site of laser irradiation of 20 months, 1-year survival of 100%, and overall survival of 24.8 months. In addition, the safety analysis of the 27 patients showed that adverse events directly related to PDT were mild. PDT was approved in Japan for health insurance coverage as a new intraoperative therapy with the indication for malignant brain tumors in September 2013. Currently, the post-marketing investigation in the accumulated patients has been conducted, and the preparation of guidelines, holding training courses, and dissemination of information on the safe implementation of PDT using web sites and videos, have been promoted. PDT is expected to be a breakthrough for the treatment of malignant glioma as a tumor cell-selective less invasive therapy for the infiltrated functional brain area. PMID:26888042

  2. Molecular Culprits Generating Brain Tumor Stem Cells

    PubMed Central

    Oh, Se-Yeong

    2013-01-01

    Despite current advances in multimodality therapies, such as surgery, radiotherapy, and chemotherapy, the outcome for patients with high-grade glioma remains fatal. Understanding how glioma cells resist various therapies may provide opportunities for developing new therapies. Accumulating evidence suggests that the main obstacle for successfully treating high-grade glioma is the existence of brain tumor stem cells (BTSCs), which share a number of cellular properties with adult stem cells, such as self-renewal and multipotent differentiation capabilities. Owing to their resistance to standard therapy coupled with their infiltrative nature, BTSCs are a primary cause of tumor recurrence post-therapy. Therefore, BTSCs are thought to be the main glioma cells representing a novel therapeutic target and should be eliminated to obtain successful treatment outcomes. PMID:24904883

  3. Multifocal brain radionecrosis masquerading as tumor dissemination

    SciTech Connect

    Safdari, H.; Boluix, B.; Gros, C.

    1984-01-01

    The authors report on an autopsy-proven case of multifocal widespread radionecrosis involving both cerebral hemispheres and masquerading as tumor dissemination on a CT scan done 13 months after complete resection of an oligodendroglioma followed by radiation therapy. This case demonstrates that radiation damage may be present in a CT scan as a multifocal, disseminated lesion. Since the survival of brain-tumor patients who have undergone radiation therapy is prolonged by aggressive therapy, the incidence and variability of radiation-induced complications in such cases is likely to increase. For similar reasons, the radionecrosis in such cases should be taken into consideration. A short review of the CT scan findings and diagnostic and therapeutic considerations in a case of widespread radionecrosis is presented. The need for appropriate diagnosis and subsequent life-saving management is emphasized.

  4. Positron Scanner for Locating Brain Tumors

    DOE R&D Accomplishments Database

    Rankowitz, S.; Robertson, J. S.; Higinbotham, W. A.; Rosenblum, M. J.

    1962-03-01

    A system is described that makes use of positron emitting isotopes for locating brain tumors. This system inherently provides more information about the distribution of radioactivity in the head in less time than existing scanners which use one or two detectors. A stationary circular array of 32 scintillation detectors scans a horizontal layer of the head from many directions simultaneously. The data, consisting of the number of counts in all possible coincidence pairs, are coded and stored in the memory of a Two-Dimensional Pulse-Height Analyzer. A unique method of displaying and interpreting the data is described that enables rapid approximate analysis of complex source distribution patterns. (auth)

  5. Multifunctional Nanoparticles for Brain Tumor Diagnosis and Therapy

    PubMed Central

    Cheng, Yu; Morshed, Ramin; Auffinger, Brenda; Tobias, Alex L.; Lesniak, Maciej S.

    2013-01-01

    Brain tumors are a diverse group of neoplasms that often carry a poor prognosis for patients. Despite tremendous efforts to develop diagnostic tools and therapeutic avenues, the treatment of brain tumors remains a formidable challenge in the field of neuro-oncology. Physiological barriers including the blood-brain barrier result in insufficient accumulation of therapeutic agents at the site of a tumor, preventing adequate destruction of malignant cells. Furthermore, there is a need for improvements in brain tumor imaging to allow for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional nanoparticles offer the potential to improve upon many of these issues and may lead to breakthroughs in brain tumor management. In this review, we discuss the diagnostic and therapeutic applications of nanoparticles for brain tumors with an emphasis on innovative approaches in tumor targeting, tumor imaging, and therapeutic agent delivery. Clinically feasible nanoparticle administration strategies for brain tumor patients are also examined. Furthermore, we address the barriers towards clinical implementation of multifunctional nanoparticles in the context of brain tumor management. PMID:24060923

  6. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

    PubMed Central

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

  7. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring.

    PubMed

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

  8. Causal Markov random field for brain MR image segmentation.

    PubMed

    Razlighi, Qolamreza R; Orekhov, Aleksey; Laine, Andrew; Stern, Yaakov

    2012-01-01

    We propose a new Bayesian classifier, based on the recently introduced causal Markov random field (MRF) model, Quadrilateral MRF (QMRF). We use a second order inhomogeneous anisotropic QMRF to model the prior and likelihood probabilities in the maximum a posteriori (MAP) classifier, named here as MAP-QMRF. The joint distribution of QMRF is given in terms of the product of two dimensional clique distributions existing in its neighboring structure. 20 manually labeled human brain MR images are used to train and assess the MAP-QMRF classifier using the jackknife validation method. Comparing the results of the proposed classifier and FreeSurfer on the Dice overlap measure shows an average gain of 1.8%. We have performed a power analysis to demonstrate that this increase in segmentation accuracy substantially reduces the number of samples required to detect a 5% change in volume of a brain region.

  9. CAUSAL MARKOV RANDOM FIELD FOR BRAIN MR IMAGE SEGMENTATION

    PubMed Central

    Razlighi, Qolamreza R.; Orekhov, Aleksey; Laine, Andrew; Stern, Yaakov

    2013-01-01

    We propose a new Bayesian classifier, based on the recently introduced causal Markov random field (MRF) model, Quadrilateral MRF (QMRF). We use a second order inhomogeneous anisotropic QMRF to model the prior and likelihood probabilities in the maximum a posteriori (MAP) classifier, named here as MAP-QMRF. The joint distribution of QMRF is given in terms of the product of two dimensional clique distributions existing in its neighboring structure. 20 manually labeled human brain MR images are used to train and assess the MAP-QMRF classifier using the jackknife validation method. Comparing the results of the proposed classifier and FreeSurfer on the Dice overlap measure shows an average gain of 1.8%. We have performed a power analysis to demonstrate that this increase in segmentation accuracy substantially reduces the number of samples required to detect a 5% change in volume of a brain region. PMID:23366607

  10. Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

    PubMed

    Pavliha, Denis; Mušič, Maja M; Serša, Gregor; Miklavčič, Damijan

    2013-01-01

    Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required. PMID:23936315

  11. Electroporation-Based Treatment Planning for Deep-Seated Tumors Based on Automatic Liver Segmentation of MRI Images

    PubMed Central

    Pavliha, Denis; Mušič, Maja M.; Serša, Gregor; Miklavčič, Damijan

    2013-01-01

    Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required. PMID:23936315

  12. Photodynamic therapy for implanted VX2 tumor in rabbit brains

    NASA Astrophysics Data System (ADS)

    Li, Fei; Feng, Hua; Lin, Jiangkai; Zhu, Gang; Chen, Zhi; Li, Cong-yan

    2005-07-01

    To evaluate the therapeutic effect and the safety of single photodynamic therapy (PDT) with hematoporphyrin derivative produced in China, 60 New Zealand adult rabbits with VX2 tumor implanted into the brain were divided randomly into non-PDT-group and PDT-group. 36 rabbits of the PDT-group were performed photodynamic therapy. The survival time, neurological deteriorations, intracranial pressure (ICP), histology, pathology, tumor volume and brain water content were measured. Other 12 rabbits were received hematoporphyrin derivative and light irradiation of the normal brain. The ICP, histology, pathology, and brain water content were measured. The result indicated that Simple PDT may elongate the average survival time of the rabbits with VX2 tumors significantly; kill tumor cells; cause transient brain edema and increase ICP, but it is safe to be used in treating brain tumor.

  13. Modeling of a Segmented Electrode for Desynchronizing Deep Brain Stimulation

    PubMed Central

    Buhlmann, J.; Hofmann, L.; Tass, P. A.; Hauptmann, C.

    2011-01-01

    Deep brain stimulation (DBS) is an effective therapy for medically refractory movement disorders like Parkinson’s disease. The electrodes, implanted in the target area within the human brain, generate an electric field which activates nerve fibers and cell bodies in the vicinity. Even though the different target nuclei display considerable differences in their anatomical structure, only few types of electrodes are currently commercially available. It is desirable to adjust the electric field and in particular the volume of tissue activated around the electrode with respect to the corresponding target nucleus in a such way that side effects can be reduced. Furthermore, a more selective and partial activation of the target structure is desirable for an optimal application of novel stimulation strategies, e.g., coordinated reset neuromodulation. Hence we designed a DBS electrode with a segmented design allowing a more selective activation of the target structure. We created a finite element model (FEM) of the electrode and analyzed the volume of tissue activated for this electrode design. The segmented electrode activated an area in a targeted manner, of which the dimension and position relative to the electrode could be controlled by adjusting the stimulation parameters for each electrode contact. According to our computational analysis, this directed stimulation might be superior with respect to the occurrence of side effects and it enables the application of coordinated reset neuromodulation under optimal conditions. PMID:22163220

  14. Brain tumors in man and animals: report of a workshop

    SciTech Connect

    Not Available

    1986-09-01

    This report summarizes the results of a workshop on brain tumors in man and animals. Animals, especially rodents are often used as surrogates for man to detect chemicals that have the potential to induce brain tumors in man. Therefore, the workshop was focused mainly on brain tumors in the F344 rat and B6C3F1 mouse because of the frequent use of these strains in long-term carcinogenesis studies. Over 100 brain tumors in F344 rats and more than 50 brain tumors in B6C3F1 mice were reviewed and compared to tumors found in man and domestic or companion animals. In the F344 rat, spontaneous brain tumors are uncommon, most are of glial origin, and the highly undifferentiated glioblastoma multiforme, a frequent tumor of man was not found. In the B6C3F1 mouse, brain tumors are exceedingly rare. Lipomas of the choroid plexus and meningiomas together account for more than 50% of the tumors found. Both rodent strains examined have low background rates and very little variability between control groups.

  15. Brain tumor resection guided by fluorescence imaging

    NASA Astrophysics Data System (ADS)

    Leblond, Frederic; Fontaine, Kathryn M.; Valdes, Pablo; Ji, Songbai; Pogue, Brian W.; Hartov, Alex; Roberts, David W.; Paulsen, Keith D.

    2009-02-01

    We present the methods that are being used in the scope of an on-going clinical trial designed to assess the usefulness of ALA-PpIX fluorescence imaging when used in conjunction with pre-operative MRI. The overall objective is to develop imaging-based neuronavigation approaches to aid in maximizing the completeness of brain tumor resection, thereby improving patient survival rate. In this paper we present the imaging methods that are used, emphasizing technical aspects relating to the fluorescence optical microscope, including initial validation approaches based on phantom and small-animal experiments. The surgical workflow is then described in detail based on a high-grade glioma resection we performed.

  16. Increasing brain tumor rates: is there a link to aspartame?

    PubMed

    Olney, J W; Farber, N B; Spitznagel, E; Robins, L N

    1996-11-01

    In the past two decades brain tumor rates have risen in several industrialized countries, including the United States. During this time, brain tumor data have been gathered by the National Cancer Institute from catchment areas representing 10% of the United States population. In the present study, we analyzed these data from 1975 to 1992 and found that the brain tumor increases in the United States occurred in two distinct phases, an early modest increase that may primarily reflect improved diagnostic technology, and a more recent sustained increase in the incidence and shift toward greater malignancy that must be explained by some other factor(s). Compared to other environmental factors putatively linked to brain tumors, the artificial sweetener aspartame is a promising candidate to explain the recent increase in incidence and degree of malignancy of brain tumors. Evidence potentially implicating aspartame includes an early animal study revealing an exceedingly high incidence of brain tumors in aspartame-fed rats compared to no brain tumors in concurrent controls, the recent finding that the aspartame molecule has mutagenic potential, and the close temporal association (aspartame was introduced into US food and beverage markets several years prior to the sharp increase in brain tumor incidence and malignancy). We conclude that there is need for reassessing the carcinogenic potential of aspartame.

  17. Atlas to patient registration with brain tumor based on a mesh-free method.

    PubMed

    Diaz, Idanis; Boulanger, Pierre

    2015-08-01

    Brain atlas to patient registration in the presence of tumors is a challenging task because its presence cause brain structure deformations and introduce large intensity variation between the affected areas. This large dissimilarity affects the results of traditional registration methods based on intensity or shape similarities. In order to overcome these problems, we propose a novel method that brings closer the atlas and the patient's image by simulating the mechanical behavior of brain deformation under a tumor pressure. The proposed method use a mesh-free total Lagrangian Explicit Dynamic algorithm for the simulation of atlas deformation and a data driven model of the tumor using multi-modal MRI segmentation. Experimental results look structurally very similar to the patient's image and outperform two of the top ranking algorithms.

  18. New treatment modalities for brain tumors in dogs and cats.

    PubMed

    Rossmeisl, John H

    2014-11-01

    Despite advancements in standard therapies, intracranial tumors remain a significant source of morbidity and mortality in veterinary and human medicine. Several newer approaches are gaining more widespread acceptance or are currently being prepared for translation from experimental to routine therapeutic use. Clinical trials in dogs with spontaneous brain tumors have contributed to the development and human translation of several novel therapeutic brain tumor approaches. PMID:25441624

  19. Yoga Therapy in Treating Patients With Malignant Brain Tumors

    ClinicalTrials.gov

    2015-07-27

    Adult Anaplastic Astrocytoma; Adult Anaplastic Ependymoma; Adult Anaplastic Meningioma; Adult Anaplastic Oligodendroglioma; Adult Brain Stem Glioma; Adult Choroid Plexus Tumor; Adult Diffuse Astrocytoma; Adult Ependymoblastoma; Adult Ependymoma; Adult Giant Cell Glioblastoma; Adult Glioblastoma; Adult Gliosarcoma; Adult Grade II Meningioma; Adult Medulloblastoma; Adult Meningeal Hemangiopericytoma; Adult Mixed Glioma; Adult Oligodendroglioma; Adult Papillary Meningioma; Adult Pineal Gland Astrocytoma; Adult Pineoblastoma; Adult Pineocytoma; Adult Supratentorial Primitive Neuroectodermal Tumor (PNET); Recurrent Adult Brain Tumor

  20. Brain atlas deformation in the presence of small and large space-occupying tumors.

    PubMed

    Dawant, B M; Hartmann, S L; Pan, Shiyan; Gadamsetty, S

    2002-01-01

    Brain atlases contain a wealth of information that could be used in radiation therapy or neurosurgical planning. Until now, however, when large space-occupying tumors and lesions drastically alter the shape of brain structures and substructures, atlas-based methods have been of limited use. In this work, we present a new technique that permits a brain atlas to be warped onto image volumes in which large lesions are present. First we show that a method previously used for atlas-based segmentation of normal brains can also be used for brains with small lesions. We then present an extension of this technique for brains with large lesions. This involves several steps: a global registration to bring the two volumes into approximate correspondence; a local registration to warp the atlas onto the patient volume; the seeding of the warped atlas with a tumor model derived from patient data; and the deformation of the seeded atlas. Global registration is performed using a mutual information criterion. The method we have used for atlas warping is derived from optical flow principles. Preliminary results obtained on real patient images are presented. These results indicate that the proposed method can be used to automatically segment structures of interest in brains with gross deformation. Potential areas of application for this method include automatic labeling of critical structures for radiation therapy and presurgical planning. PMID:12173876

  1. Lassa-Vesicular Stomatitis Chimeric Virus Safely Destroys Brain Tumors

    PubMed Central

    Wollmann, Guido; Drokhlyansky, Eugene; Davis, John N.; Cepko, Connie

    2015-01-01

    ABSTRACT High-grade tumors in the brain are among the deadliest of cancers. Here, we took a promising oncolytic virus, vesicular stomatitis virus (VSV), and tested the hypothesis that the neurotoxicity associated with the virus could be eliminated without blocking its oncolytic potential in the brain by replacing the neurotropic VSV glycoprotein with the glycoprotein from one of five different viruses, including Ebola virus, Marburg virus, lymphocytic choriomeningitis virus (LCMV), rabies virus, and Lassa virus. Based on in vitro infections of normal and tumor cells, we selected two viruses to test in vivo. Wild-type VSV was lethal when injected directly into the brain. In contrast, a novel chimeric virus (VSV-LASV-GPC) containing genes from both the Lassa virus glycoprotein precursor (GPC) and VSV showed no adverse actions within or outside the brain and targeted and completely destroyed brain cancer, including high-grade glioblastoma and melanoma, even in metastatic cancer models. When mice had two brain tumors, intratumoral VSV-LASV-GPC injection in one tumor (glioma or melanoma) led to complete tumor destruction; importantly, the virus moved contralaterally within the brain to selectively infect the second noninjected tumor. A chimeric virus combining VSV genes with the gene coding for the Ebola virus glycoprotein was safe in the brain and also selectively targeted brain tumors but was substantially less effective in destroying brain tumors and prolonging survival of tumor-bearing mice. A tropism for multiple cancer types combined with an exquisite tumor specificity opens a new door to widespread application of VSV-LASV-GPC as a safe and efficacious oncolytic chimeric virus within the brain. IMPORTANCE Many viruses have been tested for their ability to target and kill cancer cells. Vesicular stomatitis virus (VSV) has shown substantial promise, but a key problem is that if it enters the brain, it can generate adverse neurologic consequences, including death. We

  2. Patients With Brain Tumors: Who Receives Postacute Occupational Therapy Services?

    PubMed

    Chan, Vincy; Xiong, Chen; Colantonio, Angela

    2015-01-01

    Data on the utilization of occupational therapy among patients with brain tumors have been limited to those with malignant tumors and small samples of patients outside North America in specialized palliative care settings. We built on this research by examining the characteristics of patients with brain tumors who received postacute occupational therapy services in Ontario, Canada, using health care administrative data. Between fiscal years 2004-2005 and 2008-2009, 3,199 patients with brain tumors received occupational therapy services in the home care setting after hospital discharge; 12.4% had benign brain tumors, 78.2% had malignant brain tumors, and 9.4% had unspecified brain tumors. However, patients with benign brain tumors were older (mean age=63.3 yr), and a higher percentage were female (65.2%). More than 90% of patients received in-home occupational therapy services. Additional research is needed to examine the significance of these differences and to identify factors that influence access to occupational therapy services in the home care setting.

  3. Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models

    PubMed Central

    Tu, Zhuowen; Narr, Katherine L.; Dollár, Piotr; Dinov, Ivo; Thompson, Paul M.; Toga, Arthur W.

    2008-01-01

    In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multi-class discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3D MRI volumes and the results obtained are encouraging. PMID:18390346

  4. Brain abnormality segmentation based on l1-norm minimization

    NASA Astrophysics Data System (ADS)

    Zeng, Ke; Erus, Guray; Tanwar, Manoj; Davatzikos, Christos

    2014-03-01

    We present a method that uses sparse representations to model the inter-individual variability of healthy anatomy from a limited number of normal medical images. Abnormalities in MR images are then defined as deviations from the normal variation. More precisely, we model an abnormal (pathological) signal y as the superposition of a normal part ~y that can be sparsely represented under an example-based dictionary, and an abnormal part r. Motivated by a dense error correction scheme recently proposed for sparse signal recovery, we use l1- norm minimization to separate ~y and r. We extend the existing framework, which was mainly used on robust face recognition in a discriminative setting, to address challenges of brain image analysis, particularly the high dimensionality and low sample size problem. The dictionary is constructed from local image patches extracted from training images aligned using smooth transformations, together with minor perturbations of those patches. A multi-scale sliding-window scheme is applied to capture anatomical variations ranging from fine and localized to coarser and more global. The statistical significance of the abnormality term r is obtained by comparison to its empirical distribution through cross-validation, and is used to assign an abnormality score to each voxel. In our validation experiments the method is applied for segmenting abnormalities on 2-D slices of FLAIR images, and we obtain segmentation results consistent with the expert-defined masks.

  5. Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel

    2014-03-01

    Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

  6. Distinctive responses of brain tumor cells to TLR2 ligands.

    PubMed

    Yoon, Hee Jung; Jeon, Sae-Bom; Koh, Han Seok; Song, Jae-Young; Kim, Sang Soo; Kim, In-Hoo; Park, Eun Jung

    2015-05-01

    Malignant brain tumor mass contains significant numbers of infiltrating glial cells that may intimately interact with tumor cells and influence cancer treatments. Understanding of characteristic discrepancies between normal GLIA and tumor cells would, therefore, be valuable for improving anticancer therapeutics. Here, we report distinct differences in toll-like receptors (TLR)-2-mediated responses between normal glia and primary brain tumor cell lines. We found that tyrosine phosphorylation of STAT1 by TLR2 ligands and its downstream events did not occur in mouse, rat, or human brain tumor cell lines, but were markedly induced in normal primary microglia and astrocytes. Using TLR2-deficient, interferon (IFN)-γ-deficient, and IFNγ-receptor-1-deficient mice, we revealed that the impaired phosphorylation of STAT1 might be linked with defective TLR2 system in tumor cells, and that a TLR2-dependent pathway, not IFNγ-receptor machinery, might be critical for tyrosine STAT1 phosphorylation by TLR2 ligands. We also found that TLR2 and its heterodimeric partners, TLR1 and 6, on brain tumor cells failed to properly respond to TLR2 ligands, and representative TLR2-dependent cellular events, such as inflammatory responses and cell death, were not detected in brain tumor cells. Similar results were obtained in in vitro and in vivo experiments using orthotopic mouse and rat brain tumor models. Collectively, these results suggest that primary brain tumor cells may exhibit a distinctive dysfunction of TLR2-associated responses, resulting in abnormal signaling and cellular events. Careful targeting of this distinctive property could serve as the basis for effective therapeutic approaches against primary brain tumors.

  7. Database-guided breast tumor detection and segmentation in 2D ultrasound images

    NASA Astrophysics Data System (ADS)

    Zhang, Jingdan; Zhou, Shaohua K.; Brunke, Shelby; Lowery, Carol; Comaniciu, Dorin

    2010-03-01

    Ultrasonography is a valuable technique for diagnosing breast cancer. Computer-aided tumor detection and segmentation in ultrasound images can reduce labor cost and streamline clinic workflows. In this paper, we propose a fully automatic system to detect and segment breast tumors in 2D ultrasound images. Our system, based on database-guided techniques, learns the knowledge of breast tumor appearance exemplified by expert annotations. For tumor detection, we train a classifier to discriminate between tumors and their background. For tumor segmentation, we propose a discriminative graph cut approach, where both the data fidelity and compatibility functions are learned discriminatively. The performance of the proposed algorithms is demonstrated on a large set of 347 images, achieving a mean contour-to-contour error of 3.75 pixels with about 4.33 seconds.

  8. Hypofractionation Regimens for Stereotactic Radiotherapy for Large Brain Tumors

    SciTech Connect

    Yuan Jiankui; Wang, Jian Z. Lo, Simon; Grecula, John C.; Ammirati, Mario; Montebello, Joseph F.; Zhang Hualin; Gupta, Nilendu; Yuh, William T.C.; Mayr, Nina A.

    2008-10-01

    Purpose: To investigate equivalent regimens for hypofractionated stereotactic radiotherapy (HSRT) for brain tumor treatment and to provide dose-escalation guidance to maximize the tumor control within the normal brain tolerance. Methods and Materials: The linear-quadratic model, including the effect of nonuniform dose distributions, was used to evaluate the HSRT regimens. The {alpha}/{beta} ratio was estimated using the Gammaknife stereotactic radiosurgery (GKSRS) and whole-brain radiotherapy experience for large brain tumors. The HSRT regimens were derived using two methods: (1) an equivalent tumor control approach, which matches the whole-brain radiotherapy experience for many fractions and merges it with the GKSRS data for few fractions; and (2) a normal-tissue tolerance approach, which takes advantages of the dose conformity and fractionation of HSRT to approach the maximal dose tolerance of the normal brain. Results: A plausible {alpha}/{beta} ratio of 12 Gy for brain tumor and a volume parameter n of 0.23 for normal brain were derived from the GKSRS and whole-brain radiotherapy data. The HSRT prescription regimens for the isoeffect of tumor irradiation were calculated. The normal-brain equivalent uniform dose decreased as the number of fractions increased, because of the advantage of fractionation. The regimens for potential dose escalation of HSRT within the limits of normal-brain tolerance were derived. Conclusions: The designed hypofractionated regimens could be used as a preliminary guide for HSRT dose prescription for large brain tumors to mimic the GKSRS experience and for dose escalation trials. Clinical studies are necessary to further tune the model parameters and validate these regimens.

  9. Automatic corpus callosum segmentation for standardized MR brain scanning

    NASA Astrophysics Data System (ADS)

    Xu, Qing; Chen, Hong; Zhang, Li; Novak, Carol L.

    2007-03-01

    Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3 healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.

  10. Statistical shape model-based segmentation of brain MRI images.

    PubMed

    Bailleul, Jonathan; Ruan, Su; Constans, Jean-Marc

    2007-01-01

    We propose a segmentation method that automatically delineates structures contours from 3D brain MRI images using a statistical shape model. We automatically build this 3D Point Distribution Model (PDM) in applying a Minimum Description Length (MDL) annotation to a training set of shapes, obtained by registration of a 3D anatomical atlas over a set of patients brain MRIs. Delineation of any structure from a new MRI image is first initialized by such registration. Then, delineation is achieved in iterating two consecutive steps until the 3D contour reaches idempotence. The first step consists in applying an intensity model to the latest shape position so as to formulate a closer guess: our model requires far less priors than standard model in aiming at direct interpretation rather than compliance to learned contexts. The second step consists in enforcing shape constraints onto previous guess so as to remove all bias induced by artifacts or low contrast on current MRI. For this, we infer the closest shape instance from the PDM shape space using a new estimation method which accuracy is significantly improved by a huge increase in the model resolution and by a depth-search in the parameter space. The delineation results we obtained are very encouraging and show the interest of the proposed framework. PMID:18003193

  11. Pediatric Brain Tumors: Genomics and Epigenomics Pave the Way.

    PubMed

    Fontebasso, Adam M; Jabado, Nada

    2015-01-01

    Primary malignant brain tumors remain a disproportionate cause of morbidity and mortality in humans. A number of studies exploring the cancer genome of brain tumors across ages using integrated genetics and epigenetics and next-generation sequencing technologies have recently emerged. This has led to considerable advances in the understanding of the basic biology and pathogenesis of brain tumors, including the most malignant and common variants in children: gliomas and medulloblastoma. Notably, studies of pediatric brain tumors have identified unexpected oncogenic pathways implicated in tumorigenesis. These range from a single pathway/molecule defect such as abnormalities of the mitogen-activated protein kinase pathway, considered to be a hallmark of pilocytic astrocytomas, to alterations in the epigenome as a critical component altered in many subgroups of high-grade brain tumors. Importantly, the type, timing, and spatial clustering of these molecular alterations provide a better understanding of the pathogenesis of the respective brain tumor they target and critical markers for therapy that will help refine pathological grading. We summarize these novel findings in pediatric brain tumors, which also are put in the context of the evolving notion of molecular pathology, now a mandated tool for proper classification and therapy assignment in the clinical setting.

  12. High Toxoplasma gondii Seropositivity among Brain Tumor Patients in Korea

    PubMed Central

    Jung, Bong-Kwang; Song, Hyemi; Kim, Min-Jae; Cho, Jaeeun; Shin, Eun-Hee; Chai, Jong-Yil

    2016-01-01

    Toxoplasma gondii is an intracellular protozoan that can modulate the environment of the infected host. An unfavorable environment modulated by T. gondii in the brain includes tumor microenvironment. Literature has suggested that T. gondii infection is associated with development of brain tumors. However, in Korea, epidemiological data regarding this correlation have been scarce. In this study, in order to investigate the relationship between T. gondii infection and brain tumor development, we investigated the seroprevalence of T. gondii among 93 confirmed brain tumor patients (various histological types, including meningioma and astrocytoma) in Korea using ELISA. The results revealed that T. gondii seropositivity among brain tumor patients (18.3%) was significantly (P<0.05) higher compared with that of healthy controls (8.6%). The seropositivity of brain tumor patients showed a significant age-tendency, i.e., higher in younger age group, compared with age-matched healthy controls (P<0.05). In conclusion, this study supports the close relationship between T. gondii infection and incidence of brain tumors. PMID:27180580

  13. Novel treatment strategies for brain tumors and metastases

    PubMed Central

    El-Habashy, Salma E.; Nazief, Alaa M.; Adkins, Chris E.; Wen, Ming Ming; El-Kamel, Amal H.; Hamdan, Ahmed M.; Hanafy, Amira S.; Terrell, Tori O.; Mohammad, Afroz S.; Lockman, Paul R.; Nounou, Mohamed Ismail

    2015-01-01

    This review summarizes patent applications in the past 5 years for the management of brain tumors and metastases. Most of the recent patents discuss one of the following strategies: the development of new drug entities that specifically target the brain cells, the blood–brain barrier and the tumor cells, tailor-designing a novel carrier system that is able to perform multitasks and multifunction as a drug carrier, targeting vehicle and even as a diagnostic tool, direct conjugation of a US FDA approved drug with a targeting moiety, diagnostic moiety or PK modifying moiety, or the use of innovative nontraditional approaches such as genetic engineering, stem cells and vaccinations. Until now, there has been no optimal strategy to deliver therapeutic agents to the CNS for the treatment of brain tumors and metastases. Intensive research efforts are actively ongoing to take brain tumor targeting, and novel and targeted CNS delivery systems to potential clinical application. PMID:24998288

  14. Computational modeling of brain tumors: discrete, continuum or hybrid?

    NASA Astrophysics Data System (ADS)

    Wang, Zhihui; Deisboeck, Thomas S.

    In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Data-driven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silico brain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.

  15. Radiosurgery-induced brain tumor. Case report.

    PubMed

    Kaido, T; Hoshida, T; Uranishi, R; Akita, N; Kotani, A; Nishi, N; Sakaki, T

    2001-10-01

    The authors describe a case of glioblastoma multiforme (GBM) associated with previous gamma knife radiosurgery for a cerebral arteriovenous malformation (AVM). A 14-year-old boy had undergone radiosurgery for an AVM, which was performed using a 201-source 60Co gamma knife system at another institution. The maximum and margin radiation doses used in the procedure were 40 and 20 Gy, respectively. One year after radiosurgery, the patient noticed onset of mild left hemiparesis due to radiation necrosis. Six and one-half years after radiosurgery, at the age of 20 years, the patient experienced an attack of generalized tonic-clonic seizure. Magnetic resonance (MR) imaging revealed the existence of a brain tumor in the right parietal lobe. The patient underwent an operation and the histological diagnosis of the lesion was GBM. Ten months following the operation, that is, 99 months postradiosurgery, this patient died. To the best of the authors' knowledge, this is the first reported case of a neoplasm induced by radiosurgery for an AVM and the second case in which it occurred following radiosurgery for intracranial disease.

  16. Cilengitide in Treating Children With Refractory Primary Brain Tumors

    ClinicalTrials.gov

    2013-09-27

    Childhood Central Nervous System Germ Cell Tumor; Childhood Choroid Plexus Tumor; Childhood Craniopharyngioma; Childhood Ependymoblastoma; Childhood Grade I Meningioma; Childhood Grade II Meningioma; Childhood Grade III Meningioma; Childhood High-grade Cerebellar Astrocytoma; Childhood High-grade Cerebral Astrocytoma; Childhood Infratentorial Ependymoma; Childhood Low-grade Cerebellar Astrocytoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Medulloepithelioma; Childhood Mixed Glioma; Childhood Oligodendroglioma; Childhood Supratentorial Ependymoma; Recurrent Childhood Brain Stem Glioma; Recurrent Childhood Brain Tumor; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Pineoblastoma; Recurrent Childhood Subependymal Giant Cell Astrocytoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Childhood Visual Pathway and Hypothalamic Glioma

  17. Uranyl phthalocyanines show promise in the treatment of brain tumors

    NASA Technical Reports Server (NTRS)

    Frigerio, N. A.

    1967-01-01

    Processes synthesize sulfonated and nonsulfonated uranyl phthalocyanines for application in neutron therapy of brain tumors. Tests indicate that the compounds are advantageous over the previously used boron and lithium compounds.

  18. Childhood Brain and Spinal Cord Tumors Treatment Overview

    MedlinePlus

    ... before the cancer is diagnosed and continue for months or years. Childhood brain and spinal cord tumors ... after treatment. Some cancer treatments cause side effects months or years after treatment has ended. These are ...

  19. Treatment of Newly Diagnosed and Recurrent Childhood Brain Tumors

    MedlinePlus

    ... before the cancer is diagnosed and continue for months or years. Childhood brain and spinal cord tumors ... after treatment. Some cancer treatments cause side effects months or years after treatment has ended. These are ...

  20. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    PubMed Central

    Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used. PMID:27656121

  1. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results.

    PubMed

    Lyden, Hannah; Gimbel, Sarah I; Del Piero, Larissa; Tsai, A Bryna; Sachs, Matthew E; Kaplan, Jonas T; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.

  2. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    PubMed Central

    Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.

  3. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results.

    PubMed

    Lyden, Hannah; Gimbel, Sarah I; Del Piero, Larissa; Tsai, A Bryna; Sachs, Matthew E; Kaplan, Jonas T; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used. PMID:27656121

  4. [Tumor Cells and Micro-environment in Brain Metastases].

    PubMed

    Zhong, Wen; Hu, Chengping

    2016-09-20

    Improvements in survival and quality of life of patients with lung cancer had been achieved due to the progression of early diagnosis and precision medicine at recent years, however, until now, treatments targeted at lesions in central nervous system are far from satisfying, thus threatening livelihood of patients involved. After all, in the issue of prophylaxis and therapeutics of brain metastases, it is crucial to learn about the biological behavior of tumor cells in brain metastases and its mechanism underlying, and the hypothesis "seed and soil", that is, tumor cells would generate series of adaptive changes to fit in the new environment, is liable to help explain this process well. In this assay, we reviewed documents concerning tumor cells, brain micro-environments and their interactions in brain metastases, aiming to provide novel insight into the treatments of brain metastases. PMID:27666556

  5. Metastatic brain tumor from urothelial carcinoma of the prostatic urethra

    PubMed Central

    Morita, Kohei; Oda, Masashi; Koyanagi, Masaomi; Saiki, Masaaki

    2016-01-01

    Background: Urothelial carcinoma occurs in the bladder, upper urinary tract, and lower urinary tract, including prostatic urethra. A majority of the reported cases of intracranial metastasis from urothelial carcinoma originates from the bladder and upper urinary tract. Brain metastasis from urothelial carcinoma of the prostatic urethra has not yet been reported in the literature. Case Description: A 72-year-old male presented with a metastatic brain tumor and a 3-year history of urothelial carcinoma of the prostatic urethra treated with cystourethrectomy and chemotherapy with gemcitabine-cisplatin. Pathological diagnosis for tumor removal was compatible with metastatic brain tumor from urothelial carcinoma. Conclusion: Brain metastasis from urothelial carcinoma of the prostatic urethra has not yet been reported in the literature. It is an extremely rare case, however, we should be careful of brain metastasis during follow-up for urothelial carcinoma in the lower urinary tract. PMID:27512612

  6. Radiation therapy options for management of the brain tumor patient.

    PubMed

    Lamb, S A

    1995-03-01

    Radiation therapy rarely cures malignant brain tumors; however, it is the best treatment available at present. Refinement of radiation delivery systems must continue in order to minimize normal tissue injury and to maximize the quality of life. Multimodal therapy designed to attack cancer at its genetic makeup holds great promise. Radiation therapy will always remain one of the forms of therapy used to treat malignant brain tumors.

  7. FDTD analysis of a noninvasive hyperthermia system for brain tumors

    PubMed Central

    2012-01-01

    Background Hyperthermia is considered one of the new therapeutic modalities for cancer treatment and is based on the difference in thermal sensitivity between healthy tissues and tumors. During hyperthermia treatment, the temperature of the tumor is raised to 40–45°C for a definite period resulting in the destruction of cancer cells. This paper investigates design, modeling and simulation of a new non-invasive hyperthermia applicator system capable of effectively heating deep seated as well as superficial brain tumors using inexpensive, simple, and easy to fabricate components without harming surrounding healthy brain tissues. Methods The proposed hyperthermia applicator system is composed of an air filled partial half ellipsoidal chamber, a patch antenna, and a head model with an embedded tumor at an arbitrary location. The irradiating antenna is placed at one of the foci of the hyperthermia chamber while the center of the brain tumor is placed at the other focus. The finite difference time domain (FDTD) method is used to compute both the SAR patterns and the temperature distribution in three different head models due to two different patch antennas at a frequency of 915 MHz. Results The obtained results suggest that by using the proposed noninvasive hyperthermia system it is feasible to achieve sufficient and focused energy deposition and temperature rise to therapeutic values in deep seated as well as superficial brain tumors without harming surrounding healthy tissue. Conclusions The proposed noninvasive hyperthermia system proved suitable for raising the temperature in tumors embedded in the brain to therapeutic values by carefully selecting the systems components. The operator of the system only needs to place the center of the brain tumor at a pre-specified location and excite the antenna at a single frequency of 915 MHz. Our study may provide a basis for a clinical applicator prototype capable of heating brain tumors. PMID:22891953

  8. Emerging insights into barriers to effective brain tumor therapeutics.

    PubMed

    Woodworth, Graeme F; Dunn, Gavin P; Nance, Elizabeth A; Hanes, Justin; Brem, Henry

    2014-01-01

    There is great promise that ongoing advances in the delivery of therapeutics to the central nervous system (CNS) combined with rapidly expanding knowledge of brain tumor patho-biology will provide new, more effective therapies. Brain tumors that form from brain cells, as opposed to those that come from other parts of the body, rarely metastasize outside of the CNS. Instead, the tumor cells invade deep into the brain itself, causing disruption in brain circuits, blood vessel and blood flow changes, and tissue swelling. Patients with the most common and deadly form, glioblastoma (GBM) rarely live more than 2 years even with the most aggressive treatments and often with devastating neurological consequences. Current treatments include maximal safe surgical removal or biopsy followed by radiation and chemotherapy to address the residual tumor mass and invading tumor cells. However, delivering effective and sustained treatments to these invading cells without damaging healthy brain tissue is a major challenge and focus of the emerging fields of nanomedicine and viral and cell-based therapies. New treatment strategies, particularly those directed against the invasive component of this devastating CNS disease, are sorely needed. In this review, we (1) discuss the history and evolution of treatments for GBM, (2) define and explore three critical barriers to improving therapeutic delivery to invasive brain tumors, specifically, the neuro-vascular unit as it relates to the blood brain barrier, the extra-cellular space in regard to the brain penetration barrier, and the tumor genetic heterogeneity and instability in association with the treatment efficacy barrier, and (3) identify promising new therapeutic delivery approaches that have the potential to address these barriers and create sustained, meaningful efficacy against GBM. PMID:25101239

  9. Emerging Insights into Barriers to Effective Brain Tumor Therapeutics

    PubMed Central

    Woodworth, Graeme F.; Dunn, Gavin P.; Nance, Elizabeth A.; Hanes, Justin; Brem, Henry

    2014-01-01

    There is great promise that ongoing advances in the delivery of therapeutics to the central nervous system (CNS) combined with rapidly expanding knowledge of brain tumor patho-biology will provide new, more effective therapies. Brain tumors that form from brain cells, as opposed to those that come from other parts of the body, rarely metastasize outside of the CNS. Instead, the tumor cells invade deep into the brain itself, causing disruption in brain circuits, blood vessel and blood flow changes, and tissue swelling. Patients with the most common and deadly form, glioblastoma (GBM) rarely live more than 2 years even with the most aggressive treatments and often with devastating neurological consequences. Current treatments include maximal safe surgical removal or biopsy followed by radiation and chemotherapy to address the residual tumor mass and invading tumor cells. However, delivering effective and sustained treatments to these invading cells without damaging healthy brain tissue is a major challenge and focus of the emerging fields of nanomedicine and viral and cell-based therapies. New treatment strategies, particularly those directed against the invasive component of this devastating CNS disease, are sorely needed. In this review, we (1) discuss the history and evolution of treatments for GBM, (2) define and explore three critical barriers to improving therapeutic delivery to invasive brain tumors, specifically, the neuro-vascular unit as it relates to the blood brain barrier, the extra-cellular space in regard to the brain penetration barrier, and the tumor genetic heterogeneity and instability in association with the treatment efficacy barrier, and (3) identify promising new therapeutic delivery approaches that have the potential to address these barriers and create sustained, meaningful efficacy against GBM. PMID:25101239

  10. Brain and Spinal Tumors: Hope through Research

    MedlinePlus

    ... of the CNS. Some tools used in the operating room include a surgical microscope, the endoscope (a ... cells, which support other brain function. central nervous system (CNS)—the brain and spinal cord. cerebrospinal fluid ( ...

  11. Infant brain tumors: a neuropathologic population-based institutional reappraisal.

    PubMed

    Dunham, Christopher; Pillai, Shibu; Steinbok, Paul

    2012-10-01

    The factors that impact the long-term functional outcome for infants with brain tumor are unclear. The clinicopathologic features of all infant brain tumors occurring at our institution (1982-2005) were reexamined to explore the factors influencing prognosis. The details of the neuropathologic review are reported herein. Thirty-five cases were identified and included 7 astrocytomas (6 low grade and 1 glioblastoma), 6 atypical teratoid rhabdoid tumors, 5 choroid plexus papillomas, 4 ependymomas (3 anaplastic), 4 teratomas (3 immature), 2 supratentorial primitive neuroectodermal tumors, 2 gangliogliomas, 2 desmoplastic tumors of infancy, and 1 each of "medulloblastoma with extensive nodularity," adamantinomatous craniopharyngioma, and 1 "malignancy not otherwise specified." The original diagnosis was changed in 8 cases (23%), and atypical teratoid rhabdoid tumors was the most common revision (n = 5). Case 9 was unusual in that both the patient and her 2-year-old sister displayed INI-1 immunonegative posterior fossa tumors and extended survival. Tumor grade was altered in 6 cases (17%), the most significant instance being the downgrading from the World Health Organization grade IV to I (case 18: supratentorial primitive neuroectodermal tumors to desmoplastic tumors of infancy). As opposed to other reports in the literature, our cohort contained a substantially higher frequency of atypical teratoid rhabdoid tumors and a lower frequency of medulloblastoma. Changes in the histologic diagnosis/grade in a significant subset of cases most likely reflect the continual evolution of brain tumor classification schemes. INI-1 immunohistochemistry was instrumental in the pathologic assessment of select cases and raised the possibility that atypical teratoid rhabdoid tumors may be the most common infant brain malignancy.

  12. Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

    NASA Astrophysics Data System (ADS)

    Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

    2015-03-01

    Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

  13. Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

    NASA Astrophysics Data System (ADS)

    Jafari, Mehdi; Kasaei, Shohreh

    2011-12-01

    Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

  14. Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

    NASA Astrophysics Data System (ADS)

    Jafari, Mehdi; Kasaei, Shohreh

    2012-01-01

    Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

  15. Imaging of Brain Tumors With Paramagnetic Vesicles Targeted to Phosphatidylserine

    PubMed Central

    Winter, Patrick M.; Pearce, John; Chu, Zhengtao; McPherson, Christopher M.; Takigiku, Ray; Lee, Jing-Huei; Qi, Xiaoyang

    2014-01-01

    Purpose To investigate paramagnetic saposin C and dioleylphosphatidylserine (SapC-DOPS) vesicles as a targeted contrast agent for imaging phosphatidylserine (PS) expressed by glioblastoma multiforme (GBM) tumors. Materials and Methods Gd-DTPA-BSA/SapC-DOPS vesicles were formulated, and the vesicle diameter and relaxivity were measured. Targeting of Gd-DTPA-BSA/ SapC-DOPS vesicles to tumor cells in vitro and in vivo was compared with nontargeted paramagnetic vesicles (lacking SapC). Mice with GBM brain tumors were imaged at 3, 10, 20, and 24 h postinjection to measure the relaxation rate (R1) in the tumor and the normal brain. Results The mean diameter of vesicles was 175 nm, and the relaxivity at 7 Tesla was 3.32 (s*mM)−1 relative to the gadolinium concentration. Gd-DTPA-BSA/SapC-DOPS vesicles targeted cultured cancer cells, leading to an increased R1 and gadolinium level in the cells. In vivo, Gd-DTPA-BSA/SapC-DOPS vesicles produced a 9% increase in the R1 of GBM brain tumors in mice 10 h postinjection, but only minimal changes (1.2% increase) in the normal brain. Nontargeted paramagnetic vesicles yielded minimal change in the tumor R1 at 10 h postinjection (1.3%). Conclusion These experiments demonstrate that Gd-DTPA-BSA/SapC-DOPS vesicles can selectively target implanted brain tumors in vivo, providing noninvasive mapping of the cancer biomarker PS. PMID:24797437

  16. Brain tumor imaging of rat fresh tissue using terahertz spectroscopy

    PubMed Central

    Yamaguchi, Sayuri; Fukushi, Yasuko; Kubota, Oichi; Itsuji, Takeaki; Ouchi, Toshihiko; Yamamoto, Seiji

    2016-01-01

    Tumor imaging by terahertz spectroscopy of fresh tissue without dye is demonstrated using samples from a rat glioma model. The complex refractive index spectrum obtained by a reflection terahertz time-domain spectroscopy system can discriminate between normal and tumor tissues. Both the refractive index and absorption coefficient of tumor tissues are higher than those of normal tissues and can be attributed to the higher cell density and water content of the tumor region. The results of this study indicate that terahertz technology is useful for detecting brain tumor tissue. PMID:27456312

  17. Brain tumor imaging of rat fresh tissue using terahertz spectroscopy

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Sayuri; Fukushi, Yasuko; Kubota, Oichi; Itsuji, Takeaki; Ouchi, Toshihiko; Yamamoto, Seiji

    2016-07-01

    Tumor imaging by terahertz spectroscopy of fresh tissue without dye is demonstrated using samples from a rat glioma model. The complex refractive index spectrum obtained by a reflection terahertz time-domain spectroscopy system can discriminate between normal and tumor tissues. Both the refractive index and absorption coefficient of tumor tissues are higher than those of normal tissues and can be attributed to the higher cell density and water content of the tumor region. The results of this study indicate that terahertz technology is useful for detecting brain tumor tissue.

  18. Automatic co-segmentation of lung tumor based on random forest in PET-CT images

    NASA Astrophysics Data System (ADS)

    Jiang, Xueqing; Xiang, Dehui; Zhang, Bin; Zhu, Weifang; Shi, Fei; Chen, Xinjian

    2016-03-01

    In this paper, a fully automatic method is proposed to segment the lung tumor in clinical 3D PET-CT images. The proposed method effectively combines PET and CT information to make full use of the high contrast of PET images and superior spatial resolution of CT images. Our approach consists of three main parts: (1) initial segmentation, in which spines are removed in CT images and initial connected regions achieved by thresholding based segmentation in PET images; (2) coarse segmentation, in which monotonic downhill function is applied to rule out structures which have similar standardized uptake values (SUV) to the lung tumor but do not satisfy a monotonic property in PET images; (3) fine segmentation, random forests method is applied to accurately segment the lung tumor by extracting effective features from PET and CT images simultaneously. We validated our algorithm on a dataset which consists of 24 3D PET-CT images from different patients with non-small cell lung cancer (NSCLC). The average TPVF, FPVF and accuracy rate (ACC) were 83.65%, 0.05% and 99.93%, respectively. The correlation analysis shows our segmented lung tumor volumes has strong correlation ( average 0.985) with the ground truth 1 and ground truth 2 labeled by a clinical expert.

  19. Breast tumor segmentation in high resolution x-ray phase contrast analyzer based computed tomography

    SciTech Connect

    Brun, E.; Grandl, S.; Sztrókay-Gaul, A.; Gasilov, S.; Barbone, G.; Mittone, A.; Coan, P.; Bravin, A.

    2014-11-01

    Purpose: Phase contrast computed tomography has emerged as an imaging method, which is able to outperform present day clinical mammography in breast tumor visualization while maintaining an equivalent average dose. To this day, no segmentation technique takes into account the specificity of the phase contrast signal. In this study, the authors propose a new mathematical framework for human-guided breast tumor segmentation. This method has been applied to high-resolution images of excised human organs, each of several gigabytes. Methods: The authors present a segmentation procedure based on the viscous watershed transform and demonstrate the efficacy of this method on analyzer based phase contrast images. The segmentation of tumors inside two full human breasts is then shown as an example of this procedure’s possible applications. Results: A correct and precise identification of the tumor boundaries was obtained and confirmed by manual contouring performed independently by four experienced radiologists. Conclusions: The authors demonstrate that applying the watershed viscous transform allows them to perform the segmentation of tumors in high-resolution x-ray analyzer based phase contrast breast computed tomography images. Combining the additional information provided by the segmentation procedure with the already high definition of morphological details and tissue boundaries offered by phase contrast imaging techniques, will represent a valuable multistep procedure to be used in future medical diagnostic applications.

  20. Measles may be a Risk Factor for Malignant Brain Tumors

    PubMed Central

    Green, Sheryl; Rendo, Angela; Rosenzweig, Kenneth E.

    2015-01-01

    Background A possible risk factor for brain tumor might be measles, since late neurologic sequelae are part of measles pathology. Subacute sclerosing panencephalitis, a devastating neurologic illness, is prone to develop years after measles infection. Methods Because measles damage to the brain might increase the risk of brain tumor, we examined the relationship of measles incidence in 1960 and brain tumor incidence in 50 US States and the District of Columbia, 2004-2007. Data on number of cases of measles by state in 1960 are from the Morbidity and Mortality Weekly Report. In 1960 measles was a childhood illness. We calculated measles incidence by obtaining the population of each state from the 1960 US Census and then age adjusting our results to the cumulative percent of the state population under age 21, since this would have been the measles-infected group. Data on the percentage white population by state are from the US Census (www.census.gov). Age-adjusted incidence (to the 2000 US standard population) of brain tumors is from the Central Brain Tumor Registry of the United States 2011 report. Results There was a significant correlation between 1960 measles incidence and incidence of malignant brain tumors in persons 20 and older in 2004-2007 (r=0.321, p=0.026). Because glioblastoma is more frequent in whites and males, multivariate linear regression was performed with tumor incidence as the dependent variable, measles incidence, percent white population, and sex ratio by state as independent variables. Measles incidence was significantly correlated with malignant brain tumor incidence (β=0.361, p<0.001) and independent of the effect of race (β=0.734, p<0.001) and sex ratio m/f (β=-0.478, p<0.001). There was no correlation of measles incidence with brain tumor incidence in persons younger than 20. Conclusion Inflammation is a critical component of tumor development. The inflammation of measles-induced subacute sclerosing panencephalitis, even subclinical

  1. Sox2: regulation of expression and contribution to brain tumors.

    PubMed

    Mansouri, Sheila; Nejad, Romina; Karabork, Merve; Ekinci, Can; Solaroglu, Ihsan; Aldape, Kenneth D; Zadeh, Gelareh

    2016-07-01

    Tumors of the CNS are composed of a complex mixture of neoplastic cells, in addition to vascular, inflammatory and stromal components. Similar to most other tumors, brain tumors contain a heterogeneous population of cells that are found at different stages of differentiation. The cancer stem cell hypothesis suggests that all tumors are composed of subpopulation of cells with stem-like properties, which are capable of self-renewal, display resistance to therapy and lead to tumor recurrence. One of the most important transcription factors that regulate cancer stem cell properties is SOX2. In this review, we focus on SOX2 and the complex network of signaling molecules and transcription factors that regulate its expression and function in brain tumor initiating cells. We also highlight important findings in the literature about the role of SOX2 in glioblastoma and medulloblastoma, where it has been more extensively studied. PMID:27230973

  2. Rapid and automatic detection of brain tumors in MR images

    NASA Astrophysics Data System (ADS)

    Wang, Zhengjia; Hu, Qingmao; Loe, KiaFock; Aziz, Aamer; Nowinski, Wieslaw L.

    2004-04-01

    An algorithm to automatically detect brain tumors in MR images is presented. The key concern is speed in order to process efficiently large brain image databases and provide quick outcomes in clinical setting. The method is based on study of asymmetry of the brain. Tumors cause asymmetry of the brain, so we detect brain tumors in 3D MR images using symmetry analysis of image grey levels with respect to the midsagittal plane (MSP). The MSP, separating the brain into two hemispheres, is extracted using our previously developed algorithm. By removing the background pixels, the normalized grey level histograms are calculated for both hemispheres. The similarity between these two histograms manifests the symmetry of the brain, and it is quantified by using four symmetry measures: correlation coefficient, root mean square error, integral of absolute difference (IAD), and integral of normalized absolute difference (INAD). A quantitative analysis of brain normality based on 42 patients with tumors and 55 normals is presented. The sensitivity and specificity of IAD and INAD were 83.3% and 89.1%, and 85.7% and 83.6%, respectively. The running time for each symmetry measure for a 3D 8bit MR data was between 0.1 - 0.3 seconds on a 2.4GHz CPU PC.

  3. Irinotecan and Whole-Brain Radiation Therapy in Treating Patients With Brain Metastases From Solid Tumors

    ClinicalTrials.gov

    2010-03-15

    Brain and Central Nervous System Tumors; Cognitive/Functional Effects; Long-term Effects Secondary to Cancer Therapy in Adults; Long-term Effects Secondary to Cancer Therapy in Children; Poor Performance Status; Unspecified Adult Solid Tumor, Protocol Specific; Unspecified Childhood Solid Tumor, Protocol Specific

  4. Crossing the barrier: treatment of brain tumors using nanochain particles.

    PubMed

    Karathanasis, Efstathios; Ghaghada, Ketan B

    2016-09-01

    Despite advancements in surgery and radiotherapy, the aggressive forms of brain tumors, such as gliomas, are still uniformly lethal with current therapies offering only palliation complicated by significant toxicities. Gliomas are characteristically diffuse with infiltrating edges, resistant to drugs and nearly inaccessible to systemic therapies due to the brain-tumor barrier. Currently, aggressive efforts are underway to further understand brain-tumor's microenvironment and identify brain tumor cell-specific regulators amenable to pharmacologic interventions. While new potent agents are continuously becoming available, efficient drug delivery to brain tumors remains a limiting factor. To tackle the drug delivery issues, a multicomponent chain-like nanoparticle has been developed. These nanochains are comprised of iron oxide nanospheres and a drug-loaded liposome chemically linked into a 100-nm linear, chain-like assembly with high precision. The nanochain possesses a unique ability to scavenge the tumor endothelium. By utilizing effective vascular targeting, the nanochains achieve rapid deposition on the vascular bed of glioma sites establishing well-distributed drug reservoirs on the endothelium of brain tumors. After reaching the target sites, an on-command, external low-power radiofrequency field can remotely trigger rapid drug release, due to mechanical disruption of the liposome, facilitating widespread and effective drug delivery into regions harboring brain tumor cells. Integration of the nanochain delivery system with the appropriate combination of complementary drugs has the potential to unfold the field and allow significant expansion of therapies for the disease where success is currently very limited. WIREs Nanomed Nanobiotechnol 2016, 8:678-695. doi: 10.1002/wnan.1387 For further resources related to this article, please visit the WIREs website.

  5. PDE5 inhibitors enhance tumor permeability and efficacy of chemotherapy in a rat brain tumor model.

    PubMed

    Black, Keith L; Yin, Dali; Ong, John M; Hu, Jinwei; Konda, Bindu M; Wang, Xiao; Ko, MinHee K; Bayan, Jennifer-Ann; Sacapano, Manuel R; Espinoza, Andreas; Irvin, Dwain K; Shu, Yan

    2008-09-16

    The blood-brain tumor barrier (BTB) significantly limits delivery of therapeutic concentrations of chemotherapy to brain tumors. A novel approach to selectively increase drug delivery is pharmacologic modulation of signaling molecules that regulate BTB permeability, such as those in cGMP signaling. Here we show that oral administration of sildenafil (Viagra) and vardenafil (Levitra), inhibitors of cGMP-specific PDE5, selectively increased tumor capillary permeability in 9L gliosarcoma-bearing rats with no significant increase in normal brain capillaries. Tumor-bearing rats treated with the chemotherapy agent, adriamycin, in combination with vardenafil survived significantly longer than rats treated with adriamycin alone. The selective increase in tumor capillary permeability appears to be mediated by a selective increase in tumor cGMP levels and increased vesicular transport through tumor capillaries, and could be attenuated by iberiotoxin, a selective inhibitor for calcium-dependent potassium (K(Ca)) channels, that are effectors in cGMP signaling. The effect by sildenafil could be further increased by simultaneously using another BTB "opener", bradykinin. Collectively, this data demonstrates that oral administration of PDE5 inhibitors selectively increases BTB permeability and enhances anti-tumor efficacy for a chemotherapeutic agent. These findings have significant implications for improving delivery of anti-tumor agents to brain tumors. PMID:18674521

  6. Medical management of brain tumors and the sequelae of treatment

    PubMed Central

    Schiff, David; Lee, Eudocia Q.; Nayak, Lakshmi; Norden, Andrew D.; Reardon, David A.; Wen, Patrick Y.

    2015-01-01

    Patients with malignant brain tumors are prone to complications that negatively impact their quality of life and sometimes their overall survival as well. Tumors may directly provoke seizures, hypercoagulable states with resultant venous thromboembolism, and mood and cognitive disorders. Antitumor treatments and supportive therapies also produce side effects. In this review, we discuss major aspects of supportive care for patients with malignant brain tumors, with particular attention to management of seizures, venous thromboembolism, corticosteroids and their complications, chemotherapy including bevacizumab, and fatigue, mood, and cognitive dysfunction. PMID:25358508

  7. The roles of viruses in brain tumor initiation and oncomodulation

    PubMed Central

    Kofman, Alexander; Marcinkiewicz, Lucasz; Dupart, Evan; Lyshchev, Anton; Martynov, Boris; Ryndin, Anatolii; Kotelevskaya, Elena; Brown, Jay; Schiff, David

    2012-01-01

    While some avian retroviruses have been shown to induce gliomas in animal models, human herpesviruses, specifically, the most extensively studied cytomegalovirus, and the much less studied roseolovirus HHV-6, and Herpes simplex viruses 1 and 2, currently attract more and more attention as possible contributing or initiating factors in the development of human brain tumors. The aim of this review is to summarize and highlight the most provoking findings indicating a potential causative link between brain tumors, specifically malignant gliomas, and viruses in the context of the concepts of viral oncomodulation and the tumor stem cell origin. PMID:21720806

  8. Incidence of brain tumors in rats fed aspartame.

    PubMed

    Ishii, H

    1981-03-01

    The brain tumorigenicity of aspartame (APM) and of its diketopiperazine (DKP) was studied in 860 SCL Wistar rats. APM at dietary levels of 1 g/kg, 2 gK/, 4 g/kg or APM + DKP (3:1) 4 g/kg was fed for 104 weeks. One atypical astrocytoma was found in a control rat and 2 astrocytomas, 2 oligodendrogliomas and 1 ependymoma were scattered among the 4 test groups. There was no significant difference in the incidence of brain tumors between control and test groups. It is concluded that neither AMP nor DKP caused brain tumors in rats in this study.

  9. Tumor-like lesions of the brain

    PubMed Central

    2009-01-01

    Abstract Differentiation between tumors and tumor-like lesions of the central nervous system is essential for planning adequate treatment and for estimating outcome and future prognosis. Neuroimaging fulfills an essential role in the correct differentiation between both entities. The radiologist should be aware of all non-neoplastic pathologies and diseases that may mimic tumors. High-end anatomic and functional neuroimaging tools integrating multiple modalities and clinical correlation is mandatory. In the current review, frequent tumor-like lesions are discussed. PMID:19965288

  10. An evaluative tool for preoperative planning of brain tumor resection

    NASA Astrophysics Data System (ADS)

    Coffey, Aaron M.; Garg, Ishita; Miga, Michael I.; Thompson, Reid C.

    2010-02-01

    A patient specific finite element biphasic brain model has been utilized to codify a surgeon's experience by establishing quantifiable biomechanical measures to score orientations for optimal planning of brain tumor resection. When faced with evaluating several potential approaches to tumor removal during preoperative planning, the goal of this work is to facilitate the surgeon's selection of a patient head orientation such that tumor presentation and resection is assisted via favorable brain shift conditions rather than trying to allay confounding ones. Displacement-based measures consisting of area classification of the brain surface shifting in the craniotomy region and lateral displacement of the tumor center relative to an approach vector defined by the surgeon were calculated over a range of orientations and used to form an objective function. The objective function was used in conjunction with Levenberg-Marquardt optimization to find the ideal patient orientation. For a frontal lobe tumor presentation the model predicts an ideal orientation that indicates the patient should be placed in a lateral decubitus position on the side contralateral to the tumor in order to minimize unfavorable brain shift.

  11. Optical detection of brain tumors using quantum dots

    NASA Astrophysics Data System (ADS)

    Toms, Steven A.; Daneshvar, Hamid; Muhammad, Osman; Jackson, Heather; Vogelbaum, Michael A.; Bruchez, Marcel

    2005-11-01

    Introduction: Brain tumor margin detection remains a challenging problem in the operative resection of gliomas. A novel nanoparticle, a PEGylated quantum dot, has been shown to be phagocytized by macrophages in vivo. This feature may allow quantum dots to co-localize with brain tumors and serve as an optical aid in the surgical resection of brain tumors. Methods: Sprague-Daly rats were injected intracranially with C6 gliosarcoma cell lines to establish tumors. Two weeks after implantation of brain tumors, PEGylated quantum dots emitting at 705 nm (PEG-705 QD) were injected via the tail vein. Twenty-four hours post PEG-705 QD injection, the animals were sacrificed and their tissues examined. Results: PEGylated quantum dots are avidly phagocytized by macrophages and are taken up by liver, spleen and lymph nodes. Macrophages and microglia co-localize with glioma cells, carrying the optical nanoparticle, the quantum dot. Excitation of the PEG-705 quantum dots gives off a deep red fluorescence detectable with charge coupled device (CCD) cameras, optical spectroscopy units, and in dark field fluorescence microscopy. Conclusions: PEG-705QDs co-localize with brain tumors and may serve as an optical adjunct to aid in the operative resection of gliomas. The particles may be visualized in surgery with CCD cameras or detected by optical spectroscopy.

  12. Possibilities of new therapeutic strategies in brain tumors.

    PubMed

    Bouffet, Eric; Tabori, Uri; Huang, Annie; Bartels, Ute

    2010-06-01

    Advances in the management of pediatric brain tumors have been less successful than in other areas of pediatric oncology. This gap in outcome is essentially related to specific aspects of these tumors in this age group such as the fact that the surrounding brain is still developing, vital structures limit aggressive attempts at removing infiltrating lesions, drug penetration into the central nervous system is often poor and short and long term toxicities of some treatments to the surrounding brain are significant. This review describes new therapeutic strategies and their impact in the pediatric neuro-oncology practice. Although the number of new active antineoplastic agents has been limited during the last decade, significant improvements in the chemotherapeutic management of pediatric brain tumors have been observed. These relate to the optimization of chemotherapy protocols, the development of new schedules of administration such as metronomic schedules, sequential high dose chemotherapy, concomitant administration of chemotherapy and radiation, or the introduction of intrathecal or intraventricular chemotherapy in specific protocols. Technological advances in radiotherapy allow delivering optimal doses to the target volume while decreasing the volume of normal surrounding tissue receiving radiation. As a consequence, conformal radiation therapy currently plays a major role in the management of several pediatric brain tumors, including in infants where radiation has been traditionally avoided. The role of molecularly targeted agents is still unclear and a number of phase I and II trials are ongoing to better define the future of these new therapies in pediatric brain tumors.

  13. Brain tumor modeling: glioma growth and interaction with chemotherapy

    NASA Astrophysics Data System (ADS)

    Banaem, Hossein Y.; Ahmadian, Alireza; Saberi, Hooshangh; Daneshmehr, Alireza; Khodadad, Davood

    2011-10-01

    In last decade increasingly mathematical models of tumor growths have been studied, particularly on solid tumors which growth mainly caused by cellular proliferation. In this paper we propose a modified model to simulate the growth of gliomas in different stages. Glioma growth is modeled by a reaction-advection-diffusion. We begin with a model of untreated gliomas and continue with models of polyclonal glioma following chemotherapy. From relatively simple assumptions involving homogeneous brain tissue bounded by a few gross anatomical landmarks (ventricles and skull) the models have been expanded to include heterogeneous brain tissue with different motilities of glioma cells in grey and white matter. Tumor growth is characterized by a dangerous change in the control mechanisms, which normally maintain a balance between the rate of proliferation and the rate of apoptosis (controlled cell death). Result shows that this model closes to clinical finding and can simulate brain tumor behavior properly.

  14. Clinical application of PET for the evaluation of brain tumors

    SciTech Connect

    Coleman, R.E.; Hoffman, J.M.; Hanson, M.W.; Sostman, H.D.; Schold, S.C. )

    1991-04-01

    The combination of FDG and PET has demonstrated clinical utility in the evaluation of patients with brain tumors. At the time of diagnosis, FDG PET provides information concerning the degree of malignancy and patient prognosis. After therapy, FDG PET is able to assess persistence of tumor, determine degree of malignancy, monitor progression, differentiate recurrence from necrosis, and assess prognosis. Other studies using PET provide information that may be clinically useful. Determination of tumor blood flow and permeability of the blood-brain barrier may help in the selection of appropriate therapy. Amino acid imaging using 11C-methionine is being evaluated in patients with brain tumors and provides different information than FDG imaging.52 references.

  15. Factors affecting intellectual outcome in pediatric brain tumor patients

    SciTech Connect

    Ellenberg, L.; McComb, J.G.; Siegel, S.E.; Stowe, S.

    1987-11-01

    A prospective study utilizing repeated intellectual testing was undertaken in 73 children with brain tumors consecutively admitted to Childrens Hospital of Los Angeles over a 3-year period to determine the effect of tumor location, extent of surgical resection, hydrocephalus, age of the child, radiation therapy, and chemotherapy on cognitive outcome. Forty-three patients were followed for at least two sequential intellectual assessments and provide the data for this study. Children with hemispheric tumors had the most general cognitive impairment. The degree of tumor resection, adequately treated hydrocephalus, and chemotherapy had no bearing on intellectual outcome. Age of the child affected outcome mainly as it related to radiation. Whole brain radiation therapy was associated with cognitive decline. This was especially true in children below 7 years of age, who experienced a very significant loss of function after whole brain radiation therapy.

  16. Neuromorphometry of primary brain tumors by magnetic resonance imaging.

    PubMed

    Hevia-Montiel, Nidiyare; Rodriguez-Perez, Pedro I; Lamothe-Molina, Paul J; Arellano-Reynoso, Alfonso; Bribiesca, Ernesto; Alegria-Loyola, Marco A

    2015-04-01

    Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of [Formula: see text], whereas oligodendrogliomas exhibit a mean of [Formula: see text]. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of [Formula: see text], and the necrotic region presented a mean of [Formula: see text]. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor. PMID:26158107

  17. Neuromorphometry of primary brain tumors by magnetic resonance imaging

    PubMed Central

    Hevia-Montiel, Nidiyare; Rodriguez-Perez, Pedro I.; Lamothe-Molina, Paul J.; Arellano-Reynoso, Alfonso; Bribiesca, Ernesto; Alegria-Loyola, Marco A.

    2015-01-01

    Abstract. Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of 973±14, whereas oligodendrogliomas exhibit a mean of 942±21. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of 919±43, and the necrotic region presented a mean of 869±66. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor. PMID:26158107

  18. Rho GTPases in primary brain tumor malignancy and invasion.

    PubMed

    Khalil, Bassem D; El-Sibai, Mirvat

    2012-07-01

    Gliomas are the most common type of malignant primary brain tumor in humans, accounting for 80 % of malignant cases. Expression and activity of Rho GTPases, which coordinate several cellular processes including cell-cycle progression and cell migration, are commonly altered in many types of primary brain tumor. Here we review the suggested effects of deregulated Rho GTPase signaling on brain tumor malignancy, highlighting the controversy in the field. For instance, whereas expression of RhoA and RhoB has been found to be significantly reduced in astrocytic tumors, other studies have reported Rho-dependent LPA-induced migration in glioma cells. Moreover, whereas the Rac1 expression level has been found to be reduced in astrocytic tumor, it was overexpressed and induced invasion in medulloblastoma tumors. In addition to the Rho GTPases themselves, several of their downstream effectors (including ROCK, mDia, and N-WASP) and upstream regulators (including GEFs, GAPs, PI3K, and PTEN) have also been implicated in primary brain tumors.

  19. Neuromorphometry of primary brain tumors by magnetic resonance imaging.

    PubMed

    Hevia-Montiel, Nidiyare; Rodriguez-Perez, Pedro I; Lamothe-Molina, Paul J; Arellano-Reynoso, Alfonso; Bribiesca, Ernesto; Alegria-Loyola, Marco A

    2015-04-01

    Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of [Formula: see text], whereas oligodendrogliomas exhibit a mean of [Formula: see text]. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of [Formula: see text], and the necrotic region presented a mean of [Formula: see text]. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.

  20. A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.

    PubMed

    Zarinbal, M; Fazel Zarandi, M H; Turksen, I B; Izadi, M

    2015-10-01

    The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

  1. Orthotopic models of pediatric brain tumors in zebrafish.

    PubMed

    Eden, C J; Ju, B; Murugesan, M; Phoenix, T N; Nimmervoll, B; Tong, Y; Ellison, D W; Finkelstein, D; Wright, K; Boulos, N; Dapper, J; Thiruvenkatam, R; Lessman, C A; Taylor, M R; Gilbertson, R J

    2015-03-26

    High-throughput screens (HTS) of compound toxicity against cancer cells can identify thousands of potential new drug-leads. But only limited numbers of these compounds can progress to expensive and labor-intensive efficacy studies in mice, creating a 'bottle neck' in the drug development pipeline. Approaches that triage drug-leads for further study are greatly needed. Here we provide an intermediary platform between HTS and mice by adapting mouse models of pediatric brain tumors to grow as orthotopic xenografts in the brains of zebrafish. Freshly isolated mouse ependymoma, glioma and choroid plexus carcinoma cells expressing red fluorescence protein were conditioned to grow at 34 °C. Conditioned tumor cells were then transplanted orthotopically into the brains of zebrafish acclimatized to ambient temperatures of 34 °C. Live in vivo fluorescence imaging identified robust, quantifiable and reproducible brain tumor growth as well as spinal metastasis in zebrafish. All tumor xenografts in zebrafish retained the histological characteristics of the corresponding parent mouse tumor and efficiently recruited fish endothelial cells to form a tumor vasculature. Finally, by treating zebrafish harboring ERBB2-driven gliomas with an appropriate cytotoxic chemotherapy (5-fluorouracil) or tyrosine kinase inhibitor (erlotinib), we show that these models can effectively assess drug efficacy. Our data demonstrate, for the first time, that mouse brain tumors can grow orthotopically in fish and serve as a platform to study drug efficacy. As large cohorts of brain tumor-bearing zebrafish can be generated rapidly and inexpensively, these models may serve as a powerful tool to triage drug-leads from HTS for formal efficacy testing in mice. PMID:24747973

  2. Simultaneous detection of multiple elastic surfaces with application to tumor segmentation in CT images

    NASA Astrophysics Data System (ADS)

    Li, Kang; Jolly, Marie-Pierre

    2008-03-01

    We present a new semi-supervised method for segmenting multiple interrelated object boundaries with spherical topology in volumetric images. The core of our method is a novel graph-theoretic algorithm that simultaneously detects multiple surfaces under smoothness, distance, and elasticity constraints. The algorithm computes the global optimum of an objective function that incorporates boundary, regional and surface elasticity information. A single straight line drawn by the user in a cross-sectional slice is the sole user input, which roughly indicates the extent of the object. We employ a multi-seeded Dijkstra-based range competition algorithm to pre-segment the object on two orthogonal multiplanar reformatted (MPR) planes that pass through the input line. Based on the 2D pre-segmentation results, we estimate the object and background intensity histograms, and employ an adaptive mean-shift mode-seeking process on the object histogram to automatically determine the number of surface layers to be segmented. The final multiple-surface segmentation is performed in an ellipsoidal coordinate frame constructed by an automated ellipsoid fitting procedure. We apply our method to the segmentation of liver lesions with necrosis or calcification, and various other tumors in CT images. For liver tumor segmentation, our method can simultaneously delineate both tumor and necrosis boundaries. This capability is unprecedented and is valuable for cancer diagnosis, treatment planning, and evaluation.

  3. The therapy of infantile malignant brain tumors: current status?

    PubMed

    Kalifa, Chantal; Grill, Jacques

    2005-12-01

    Malignant brain tumors are not uncommon in infants as their occurrence before the age of three represents 20-25% of all malignant brain tumors in childhood [1]. Genetic predisposition to infantile malignant brain tumors are known in Gorlin syndrome for example who present with desmoplastic medulloblastoma in about 5% of the affected patients. In addition, sequelae from tumor and its treatment are more severe at this age [2]. Thus, malignant brain tumors represent a true therapeutic challenge in neuro-oncology. Before the era of modern imaging and modern neurosurgery these malignant brain tumors were misdiagnosed or could not benefit of the surgical procedures as well as older children because of increased risks in this age group. Since the end of the 80s, noninvasive imaging procedures produce accurate diagnosis of brain tumors and improvement in neurosurgery, neuroanesthesia and perioperative intensive care permit safe tumor resections or at least biopsies. Consequently, the pediatric oncologists are more often confronted with very young children who need a complementary treatment. Before the development of specific approaches for this age group, these children received the same kind of treatment than the older children did, but their survival and quality of life were significantly worse. The reasons of these poor results were probably due in part to the fear of late effects induced by radiation therapy, leading to decrease the necessary doses of irradiation which increased treatment failures without avoiding treatment related complications [3]. At the end of the 80s, pilot studies were performed using postoperative chemotherapy in young medulloblastoma patients. Van Eys treated 12 selected children with medulloblastoma with MOPP regimen and without irradiation; 8 of them were reported to be long term survivors [4]. Subsequently, the pediatric oncology cooperative groups studies have designed therapeutic trials for very young children with malignant brain tumors

  4. Simian virus 40 transformation, malignant mesothelioma and brain tumors

    PubMed Central

    Qi, Fang; Carbone, Michele; Yang, Haining; Gaudino, Giovanni

    2011-01-01

    Simian virus 40 (SV40) is a DNA virus isolated in 1960 from contaminated polio vaccines, that induces mesotheliomas, lymphomas, brain and bone tumors, and sarcomas, including osteosarcomas, in hamsters. These same tumor types have been found to contain SV40 DNA and proteins in humans. Mesotheliomas and brain tumors are the two tumor types that have been most consistently associated with SV40, and the range of positivity has varied about from 6 to 60%, although a few reported 100% of positivity and a few reported 0%. It appears unlikely that SV40 infection alone is sufficient to cause human malignancy, as we did not observe an epidemic of cancers following the administration of SV40-contaminated vaccines. However, it seems possible that SV40 may act as a cofactor in the pathogenesis of some tumors. In vitro and animal experiments showing cocarcinogenicity between SV40 and asbestos support this hypothesis. PMID:21955238

  5. Pediatric brain tumor treatment: growth consequences and their management.

    PubMed

    Mostoufi-Moab, Sogol; Grimberg, Adda

    2010-09-01

    Tumors of the central nervous system, the most common solid tumors of childhood, are a major source of cancer-related morbidity and mortality in children. Survival rates have improved significantly following treatment for childhood brain tumors, with this growing cohort of survivors at high risk of adverse medical and late effects. Endocrine morbidities are the most prominent disorder among the spectrum of longterm conditions, with growth hormone deficiency the most common endocrinopathy noted, either from tumor location or after cranial irradiation and treatment effects on the hypothalamic/pituitary unit. Deficiency of other anterior pituitary hormones can contribute to negative effects on growth, body image and composition, sexual function, skeletal health, and quality of life. Pediatric and adult endocrinologists often provide medical care to this increasing population. Therefore, a thorough understanding of the epidemiology and pathophysiology of growth failure as a consequence of childhood brain tumor, both during and after treatment, is necessary and the main focus of this review.

  6. The role of integrins in primary and secondary brain tumors.

    PubMed

    Schittenhelm, Jens; Tabatabai, Ghazaleh; Sipos, Bence

    2016-10-01

    The tumor environment plays an integral part in the biology of cancer, participating in tumor initiation, progression, and response to therapy. Integrins, a family of cell surface receptors, bridge the extracellular matrix to the intracellular cytoskeleton. Since their first characterization 25 years ago, a vast amount of work has been performed to understand the essential role of integrins in cell development, tissue organization, tumor growth, vessel development and their signaling mechanisms. Their potential as therapeutic targets in various types of cancer is intensively studied. In this review, we discuss the expression patterns and functional role of integrin in primary brain tumors and brain metastases, provide an overview of clinical data on integrin inhibition and their potential application in imaging and therapy of these tumors. PMID:27097828

  7. Prediction of brain tumor progression using a machine learning technique

    NASA Astrophysics Data System (ADS)

    Shen, Yuzhong; Banerjee, Debrup; Li, Jiang; Chandler, Adam; Shen, Yufei; McKenzie, Frederic D.; Wang, Jihong

    2010-03-01

    A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to tumor at a later time. In this preliminary study, we focused on the trend of brain tumor progression during three consecutive visits, i.e., visit A, B, and C. A machine learning algorithm was trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit A to visit C. Preliminary results showed that prediction for brain tumor progression is feasible. An average of 80.9% pixel-wise accuracy was achieved for tumor progression prediction at visit C.

  8. US-Cut: interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions

    NASA Astrophysics Data System (ADS)

    Egger, Jan; Voglreiter, Philip; Dokter, Mark; Hofmann, Michael; Chen, Xiaojun; Zoller, Wolfram G.; Schmalstieg, Dieter; Hann, Alexander

    2016-04-01

    Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing the user to get real-time feedback of the segmentation results. The algorithm has been developed and tested hand-in-hand by physicians and computer scientists to make sure a future practical usage in a clinical setting is feasible. To cover typical acquisitions from the clinical routine, the approach has been evaluated with dozens of datasets where the tumors are hyperechoic (brighter), hypoechoic (darker) or isoechoic (similar) in comparison to the surrounding liver tissue. Due to the interactive real-time behavior of the approach, it was possible even in difficult cases to find satisfying segmentations of the tumors within seconds and without parameter settings, and the average tumor deviation was only 1.4mm compared with manual measurements. However, the long term goal is to ease the volumetric acquisition of liver tumors in order to evaluate for treatment response. Additional aim is the registration of intraoperative US images via the interactive segmentations to the patient's pre-interventional CT acquisitions.

  9. Blood Brain Barrier: A Challenge for Effectual Therapy of Brain Tumors

    PubMed Central

    Bhowmik, Arijit; Ghosh, Mrinal Kanti

    2015-01-01

    Brain tumors are one of the most formidable diseases of mankind. They have only a fair to poor prognosis and high relapse rate. One of the major causes of extreme difficulty in brain tumor treatment is the presence of blood brain barrier (BBB). BBB comprises different molecular components and transport systems, which in turn create efflux machinery or hindrance for the entry of several drugs in brain. Thus, along with the conventional techniques, successful modification of drug delivery and novel therapeutic strategies are needed to overcome this obstacle for treatment of brain tumors. In this review, we have elucidated some critical insights into the composition and function of BBB and along with it we have discussed the effective methods for delivery of drugs to the brain and therapeutic strategies overcoming the barrier. PMID:25866775

  10. Segmentation of the brain from 3D MRI using a hierarchical active surface template

    NASA Astrophysics Data System (ADS)

    Snell, John W.; Merickel, Michael B.; Ortega, James M.; Goble, John C.; Brookeman, James R.; Kassell, Neal F.

    1994-05-01

    The accurate segmentation of the brain from three-dimensional medical imagery is important as the basis for visualization, morphometry, surgical planning and intraoperative navigation. The complex and variable nature of brain anatomy makes recognition of the brain boundaries a difficult problem and frustrates segmentation schemes based solely on local image features. We have developed a deformable surface model of the brain as a mechanism for utilizing a priori anatomical knowledge in the segmentation process. The active surface template uses an energy minimization scheme to find a globally consistent surface configuration given a set of potentially ambiguous image features. Solution of the entire 3D problem at once produces superior results to those achieved using a slice by slice approach. We have achieved good results with MR image volumes of both normal and abnormal subjects. Evaluation of the segmentation results has been performed using cadaver studies.

  11. Primary brain tumors, delta 24 and tumor metabolism. Interview by Rona Williamson.

    PubMed

    Gilbert, Mark R

    2013-04-01

    Interview by Rona Williamson, Commissioning Editor Mark R Gilbert studied medicine at the Johns Hopkins School of Medicine in Baltimore (MD, USA). He completed residency training in internal medicine and neurology at the Johns Hopkins Hospital, then was named the first Keck Foundation Fellow in Neuro-Oncology at Johns Hopkins. After 2 years on the faculty at Johns Hopkins, he moved to the University of Pittsburgh to head the Brain Tumor Program. During his tenure at Pittsburgh (PA, USA), he was named Chair of the Brain Tumor Committee of the Eastern Cooperative Oncology Group. In 1996, Dr Gilbert moved to the Emory University in Atlanta (GA, USA) to lead the Medical Neuro-Oncology Program and successfully competed for the program's membership in the New Approaches to Brain Tumor Treatment consortium. Dr Gilbert moved to the MD Anderson Cancer Center in Houston (TX, USA) in 2000 as Deputy Chair of the Department of Neuro-Oncology. During his tenure at MD Anderson, he has created two brain tumor consortia. The Collaborative Ependymoma Research Network is an international effort that is focusing research efforts on patients, both adult and pediatric, with this uncommon central nervous system tumor. The Brain Tumor Trials Collaborative is a 23-institution consortium that focuses on innovative clinical trials for primary glial malignancies. In addition, Dr Gilbert holds a leadership position in the Radiation Therapy Oncology Group and has served as the principal investigator on several large randomized brain tumor clinical trials. His research focus has been in the area of clinical and translational research for primary brain tumors. This includes novel clinical trial designs and the integration of correlative tumor biology with these clinical studies.

  12. HFE polymorphisms affect survival of brain tumor patients.

    PubMed

    Lee, Sang Y; Slagle-Webb, Becky; Sheehan, Jonas M; Zhu, Junjia; Muscat, Joshua E; Glantz, Michael; Connor, James R

    2015-03-01

    The HFE (high iron) protein plays a key role in the regulation of body iron. HFE polymorphisms (H63D and C282Y) are the common genetic variants in Caucasians. Based on frequency data, both HFE polymorphisms have been associated with increased risk in a number of cancers. The prevalence of the two major HFE polymorphisms in a human brain tumor patient populations and the impact of HFE polymorphisms on survival have not been studied. In the present study, there is no overall difference in survival by HFE genotype. However, male GBM patients with H63D HFE (H63D) have poorer overall survival than wild type HFE (WT) male GBM (p = 0.03). In GBM patients with the C282Y HFE polymorphism (C282Y), female patients have poorer survival than male patients (p = 0.05). In addition, female metastatic brain tumor patients with C282Y have shorter survival times post diagnosis than WT patients (p = 0.02) or male metastatic brain tumor patients with C282Y (p = 0.02). There is a tendency toward a lower proportion of H63D genotype in GBM patients than a non-tumor control group (p = 0.09) or other subtypes of brain tumors. In conclusion, our study suggests that HFE genotype impacts survival of brain tumor patients in a gender specific manner. We previously reported that glioma and neuroblastoma cell lines with HFE polymorphisms show greater resistance to chemo and radiotherapy. Taken together, these data suggest HFE genotype is an important consideration for evaluating and planning therapeutic strategies in brain tumor patients.

  13. Nonlinear microscopy, infrared, and Raman microspectroscopy for brain tumor analysis

    NASA Astrophysics Data System (ADS)

    Meyer, Tobias; Bergner, Norbert; Bielecki, Christiane; Krafft, Christoph; Akimov, Denis; Romeike, Bernd F. M.; Reichart, Rupert; Kalff, Rolf; Dietzek, Benjamin; Popp, Jürgen

    2011-02-01

    Contemporary brain tumor research focuses on two challenges: First, tumor typing and grading by analyzing excised tissue is of utmost importance for choosing a therapy. Second, for prognostication the tumor has to be removed as completely as possible. Nowadays, histopathology of excised tissue using haematoxylin-eosine staining is the gold standard for the definitive diagnosis of surgical pathology specimens. However, it is neither applicable in vivo, nor does it allow for precise tumor typing in those cases when only nonrepresentative specimens are procured. Infrared and Raman spectroscopy allow for very precise cancer analysis due to their molecular specificity, while nonlinear microscopy is a suitable tool for rapid imaging of large tissue sections. Here, unstained samples from the brain of a domestic pig have been investigated by a multimodal nonlinear imaging approach combining coherent anti-Stokes Raman scattering, second harmonic generation, and two photon excited fluorescence microscopy. Furthermore, a brain tumor specimen was additionally analyzed by linear Raman and Fourier transform infrared imaging for a detailed assessment of the tissue types that is required for classification and to validate the multimodal imaging approach. Hence label-free vibrational microspectroscopic imaging is a promising tool for fast and precise in vivo diagnostics of brain tumors.

  14. Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

    NASA Astrophysics Data System (ADS)

    Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

    2015-04-01

    Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

  15. Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

    SciTech Connect

    Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

    2015-04-24

    Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

  16. Combination strategies in multi-atlas image segmentation: application to brain MR data.

    PubMed

    Artaechevarria, Xabier; Munoz-Barrutia, Arrate; Ortiz-de-Solorzano, Carlos

    2009-08-01

    It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected. PMID:19228554

  17. A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

    NASA Astrophysics Data System (ADS)

    Yu, Ning; Wu, Jia; Weinstein, Susan P.; Gaonkar, Bilwaj; Keller, Brad M.; Ashraf, Ahmed B.; Jiang, YunQing; Davatzikos, Christos; Conant, Emily F.; Kontos, Despina

    2015-03-01

    Accurate and efficient automated tumor segmentation in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is highly desirable for computer-aided tumor diagnosis. We propose a novel automatic segmentation framework which incorporates mean-shift smoothing, superpixel-wise classification, pixel-wise graph-cuts partitioning, and morphological refinement. A set of 15 breast DCE-MR images, obtained from the American College of Radiology Imaging Network (ACRIN) 6657 I-SPY trial, were manually segmented to generate tumor masks (as ground truth) and breast masks (as regions of interest). Four state-of-the-art segmentation approaches based on diverse models were also utilized for comparison. Based on five standard evaluation metrics for segmentation, the proposed framework consistently outperformed all other approaches. The performance of the proposed framework was: 1) 0.83 for Dice similarity coefficient, 2) 0.96 for pixel-wise accuracy, 3) 0.72 for VOC score, 4) 0.79 mm for mean absolute difference, and 5) 11.71 mm for maximum Hausdorff distance, which surpassed the second best method (i.e., adaptive geodesic transformation), a semi-automatic algorithm depending on precise initialization. Our results suggest promising potential applications of our segmentation framework in assisting analysis of breast carcinomas.

  18. Complete nucleotide sequence of wound tumor virus genomic segments encoding nonstructural polypeptides.

    PubMed

    Anzola, J V; Dall, D J; Xu, Z K; Nuss, D L

    1989-07-01

    Sequence analysis of the genomic segments which encode the five wound tumor virus nonstructural polypeptides has been completed. The complete nucleotide sequence of segments S4 (2565 bp), S6 (1700 bp), S9 (1182 bp), and S10 (1172 bp) are presented in this report while the sequence of segment S12 (851 bp) has been described previously (T. Asamizu, D. Summers, M. B. Motika, J. V. Anzola, and D. L. Nuss, 1985, Virology 144, 398-409). Comparison of the only published sequence for another member of the genus Phytoreovirus, that of rice dwarf virus segment S10, with the combined available wound tumor virus sequence data revealed similarity with WTV segment S10: 54.9 and 30.6% at the nucleotide and amino acid level, respectively. Although wound tumor virus and rice dwarf virus differ in plant host range, tissue specificity, vector range, and disease symptom expression, the level of sequence similarity shared by the two segments suggests a common origin for these viruses. The potential use of a phytoreovirus sequence database for predicting functions of viral encoded gene products is considered.

  19. New strategies to deliver anticancer drugs to brain tumors

    PubMed Central

    Laquintana, Valentino; Trapani, Adriana; Denora, Nunzio; Wang, Fan; Gallo, James M.; Trapani, Giuseppe

    2009-01-01

    BACKGROUND Malignant brain tumors are among the most challenging to treat and at present there are no uniformly successful treatment strategies. Standard treatment regimens consist of maximal surgical resection followed by radiotherapy and chemotherapy. The limited survival advantage attributed to chemotherapy is partially due to low CNS penetration of antineoplastic agents across the blood-brain barrier (BBB). OBJECTIVE The objective of this paper is to review recent approaches to deliver anticancer drugs into primary brain tumors. METHODS Both preclinical and clinical strategies to circumvent the BBB are considered that includes chemical modification and colloidal carriers. CONCLUSION Analysis of the available data indicates that novel approaches may be useful for CNS delivery, yet an appreciation of pharmacokinetic issues, and improved knowledge of tumor biology will be needed to significantly impact drug delivery to the target site. PMID:19732031

  20. Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

    PubMed

    Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

    2014-01-01

    The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  1. Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images.

    PubMed

    Zheng, Yuanjie; Baloch, Sajjad; Englander, Sarah; Schnall, Mitchell D; Shen, Dinggang

    2007-01-01

    Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. Second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract Spatio-Temporal Enhancement Patterns (STEPs). STEPs are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. While temporal enhancement features are extracted through Fourier transform, the resulting STEP framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant Gabor textures. High accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the ROC curve.

  2. Assessing the scale of tumor heterogeneity by complete hierarchical segmentation of MRI

    NASA Astrophysics Data System (ADS)

    Gensheimer, Michael F.; Hawkins, Douglas S.; Ermoian, Ralph P.; Trister, Andrew D.

    2015-02-01

    In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment.

  3. Automated segmentation of in vivo and ex vivo mouse brain magnetic resonance images.

    PubMed

    Scheenstra, Alize E H; van de Ven, Rob C G; van der Weerd, Louise; van den Maagdenberg, Arn M J M; Dijkstra, Jouke; Reiber, Johan H C

    2009-01-01

    Segmentation of magnetic resonance imaging (MRI) data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation. PMID:19344574

  4. Circulating biomarker panels for targeted therapy in brain tumors.

    PubMed

    Tanase, Cristiana; Albulescu, Radu; Codrici, Elena; Popescu, Ionela Daniela; Mihai, Simona; Enciu, Ana Maria; Cruceru, Maria Linda; Popa, Adrian Claudiu; Neagu, Ana Iulia; Necula, Laura Georgiana; Mambet, Cristina; Neagu, Monica

    2015-01-01

    An important goal of oncology is the development of cancer risk-identifier biomarkers that aid early detection and target therapy. High-throughput profiling represents a major concern for cancer research, including brain tumors. A promising approach for efficacious monitoring of disease progression and therapy could be circulating biomarker panels using molecular proteomic patterns. Tailoring treatment by targeting specific protein-protein interactions and signaling networks, microRNA and cancer stem cell signaling in accordance with tumor phenotype or patient clustering based on biomarker panels represents the future of personalized medicine for brain tumors. Gathering current data regarding biomarker candidates, we address the major challenges surrounding the biomarker field of this devastating tumor type, exploring potential perspectives for the development of more effective predictive biomarker panels.

  5. Novel Magnetic Resonance Imaging Techniques in Brain Tumors.

    PubMed

    Nechifor, Ruben E; Harris, Robert J; Ellingson, Benjamin M

    2015-06-01

    Magnetic resonance imaging is a powerful, noninvasive imaging technique with exquisite sensitivity to soft tissue composition. Magnetic resonance imaging is primary tool for brain tumor diagnosis, evaluation of drug response assessment, and clinical monitoring of the patient during the course of their disease. The flexibility of magnetic resonance imaging pulse sequence design allows for a variety of image contrasts to be acquired, including information about magnetic resonance-specific tissue characteristics, molecular dynamics, microstructural organization, vascular composition, and biochemical status. The current review highlights recent advancements and novel approaches in MR characterization of brain tumors.

  6. Training stem cells for treatment of malignant brain tumors

    PubMed Central

    Li, Shengwen Calvin; Kabeer, Mustafa H; Vu, Long T; Keschrumrus, Vic; Yin, Hong Zhen; Dethlefs, Brent A; Zhong, Jiang F; Weiss, John H; Loudon, William G

    2014-01-01

    The treatment of malignant brain tumors remains a challenge. Stem cell technology has been applied in the treatment of brain tumors largely because of the ability of some stem cells to infiltrate into regions within the brain where tumor cells migrate as shown in preclinical studies. However, not all of these efforts can translate in the effective treatment that improves the quality of life for patients. Here, we perform a literature review to identify the problems in the field. Given the lack of efficacy of most stem cell-based agents used in the treatment of malignant brain tumors, we found that stem cell distribution (i.e., only a fraction of stem cells applied capable of targeting tumors) are among the limiting factors. We provide guidelines for potential improvements in stem cell distribution. Specifically, we use an engineered tissue graft platform that replicates the in vivo microenvironment, and provide our data to validate that this culture platform is viable for producing stem cells that have better stem cell distribution than with the Petri dish culture system. PMID:25258664

  7. Progress on the diagnosis and evaluation of brain tumors

    PubMed Central

    Gao, Huile

    2013-01-01

    Abstract Brain tumors are one of the most challenging disorders encountered, and early and accurate diagnosis is essential for the management and treatment of these tumors. In this article, diagnostic modalities including single-photon emission computed tomography, positron emission tomography, magnetic resonance imaging, and optical imaging are reviewed. We mainly focus on the newly emerging, specific imaging probes, and their potential use in animal models and clinical settings. PMID:24334439

  8. Dysphagia outcomes in patients with brain tumors undergoing inpatient rehabilitation.

    PubMed

    Wesling, Michele; Brady, Susan; Jensen, Mary; Nickell, Melissa; Statkus, Donna; Escobar, Nelson

    2003-01-01

    The purpose of this retrospective study was to compare functional dysphagia outcomes following inpatient rehabilitation for patients with brain tumors with that of patients following a stroke. Group 1 (n = 24) consisted of consecutive admissions to the brain injury program with the diagnosis of brain tumor and dysphagia. Group 2 (n = 24) consisted of matched, consecutive admissions, with the diagnosis of acute stroke and dysphagia. Group 2 was matched for age, site of lesion, and initial composite cognitive FIM score. The main outcome measures for this study included the American Speech-Language-Hearing Association (ASHA) National Outcome Measurement System (NOMS) swallowing scale, length of stay, hospital charges, and medical complications. Results showed that swallowing gains made by both groups as evaluated by the admission and discharge ASHA NOMS levels were considered to be statistically significant. The differences for length of stay, total hospital charges, and speech charges between the two groups were not considered to be statistically significant. Three patients in the brain tumor group (12.5%) demonstrated dysphagia complications of either dehydration or pneumonia during their treatment course as compared to 0% in the stroke group. This study confirms that functional dysphagia gains can be achieved for patients with brain tumors undergoing inpatient rehabilitation and that they should be afforded the same type and intensity of rehabilitation for their swallowing that is provided to patients following a stroke.

  9. Agnosias: recognition disorders in patients with brain tumors.

    PubMed

    Gainotti, Guido

    2012-06-01

    Two main varieties of recognition disorders are distinguished in neuropsychology: agnosias and semantic disorders. The term agnosias is generally used to denote recognition defects limited to a single perceptual modality (which is itself apparently intact), whereas the term semantic disorders is used to denote recognition defects involving all the sensory modalities in a roughly similar manner. Brain tumors can be one of the aetiologies underlying agnosias and semantic disorders. However, due to the heterogeneity and the rarity of recognition disorders, their investigation can be useful only to suggest or exclude the oncological nature of a brain lesion, but not to systematically monitor the clinical outcome in tumor patients. Furthermore, the relevance of recognition disorders as a hint toward a diagnosis of brain tumor varies according to the type of agnosia and of semantic disorder and the localization of the underlying brain pathology. The hypothesis that a variety of agnosia (or of semantic disorder) may be due to a neoplastic lesion can, therefore, be advanced if it is consistent with our knowledge about the usual localization and the growing patterns of different types of brain tumors.

  10. Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain.

    PubMed

    Schwarz, Christopher G; Tsui, Alex; Fletcher, Evan; Singh, Baljeet; DeCarli, Charles; Carmichael, Owen

    2011-01-01

    Automatically segmenting brain magnetic resonance images into grey matter, white matter, and cerebrospinal fluid compartments is a fundamentally important neuroimaging problem whose difficulty is heightened in the presence of aging and neurodegenerative disease. Current methods overlap greatly in terms of identifiable algorithmic components, and the impact of specific components on performance is generally unclear in important real-world scenarios involving serial scanning, multiple scanners, and neurodegenerative disease. Therefore we evaluated the impact that one such component, the Markov Random Field (MRF) optimizer that encourages spatially-smooth tissue labelings, has on brain tissue segmentation performance. Two challenging elderly data sets were used to test segmentation consistency across scanners and biological plausibility of tissue change estimates; and a simulated young brain data set was used to test accuracy against ground truth. Belief propagation (BP) and graph cuts (GC), used as the MRF optimizer component of a standardized segmentation system, provide high segmentation performance on aggregate that is competitive with end-to-end systems provided by SPM and FSL (FAST) as well as the more traditional MRF optimizer iterated conditional modes (ICM). However, the relative performance of each method varied strongly by performance criterion and differed between young and old brains. The findings emphasize the unique difficulties involved in segmenting the aging brain, and suggest that optimal algorithm components may depend in part on performance criteria.

  11. A dual neural network ensemble approach for multiclass brain tumor classification.

    PubMed

    Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

    2012-11-01

    The present study is conducted to develop an interactive computer aided diagnosis (CAD) system for assisting radiologists in multiclass classification of brain tumors. In this paper, primary brain tumors such as astrocytoma, glioblastoma multiforme, childhood tumor-medulloblastoma, meningioma and secondary tumor-metastases along with normal regions are classified by a dual level neural network ensemble. Two hundred eighteen texture and intensity features are extracted from 856 segmented regions of interest (SROIs) and are taken as input. PCA is used for reduction of dimensionality of the feature space. The study is performed on a diversified dataset of 428 post contrast T1-weighted magnetic resonance images of 55 patients. Two sets of experiments are performed. In the first experiment, random selection is used which may allow SROIs from the same patient having similar characteristics to appear in both training and testing simultaneously. In the second experiment, not even a single SROI from the same patient is common during training and testing. In the first experiment, it is observed that the dual level neural network ensemble has enhanced the overall accuracy to 95.85% compared with 91.97% of single level artificial neural network. The proposed method delivers high accuracy for each class. The accuracy obtained for each class is: astrocytoma 96.29%, glioblastoma multiforme 96.15%, childhood tumor-medulloblastoma 90%, meningioma 93.00%, secondary tumor-metastases 96.67% and normal regions 97.41%. This study reveals that dual level neural network ensemble provides better results than the single level artificial neural network. In the second experiment, overall classification accuracy of 90.4% was achieved. The generalization ability of this approach can be tested by analyzing larger datasets. The extensive training will also further improve the performance of the proposed dual network ensemble. Quantitative results obtained from the proposed method will assist the

  12. Tumor-infiltrating lymphocytes expressing IOT-10 marker. An immunohistochemical study of a series of 185 brain tumors.

    PubMed

    Zurita, M; Vaquero, J; Coca, S; Oya, S; Garcia, N

    1993-04-01

    The presence of IOT-10-positive lymphocytes among the tumor-infiltrating-lymphocyte (TIL) population was studied in a series of 185 brain tumors. In most of the tumors, IOT-10-positive lymphocytes were identified, but generally they were scarce and masked among the tumor cells, suggesting that NK-cells exercise a poor participation in the tissular response against brain tumors. Isolated tumor cells showing IOT-10-positivity were found in low-grade astrocytomas, neurinomas and medulloblastomas. IOT-10-positivity on both tumor neuropil and tumor cells was considered a characteristic finding in oligodendrogliomas. The number of IOT-10-positive NK-cells in brain metastases and in cerebellar hemangioblastomas was comparatively greater than in other types of brain tumor. Since in brain metastases, the presence of IOT-10-positive NK-cells can be related to the tissular response to an extracerebral malignancy, their considerable presence in cerebellar hemangioblastomas is an enigmatic finding that deserves further attention.

  13. Automatic delineation of tumor volumes by co-segmentation of combined PET/MR data

    NASA Astrophysics Data System (ADS)

    Leibfarth, S.; Eckert, F.; Welz, S.; Siegel, C.; Schmidt, H.; Schwenzer, N.; Zips, D.; Thorwarth, D.

    2015-07-01

    Combined PET/MRI may be highly beneficial for radiotherapy treatment planning in terms of tumor delineation and characterization. To standardize tumor volume delineation, an automatic algorithm for the co-segmentation of head and neck (HN) tumors based on PET/MR data was developed. Ten HN patient datasets acquired in a combined PET/MR system were available for this study. The proposed algorithm uses both the anatomical T2-weighted MR and FDG-PET data. For both imaging modalities tumor probability maps were derived, assigning each voxel a probability of being cancerous based on its signal intensity. A combination of these maps was subsequently segmented using a threshold level set algorithm. To validate the method, tumor delineations from three radiation oncologists were available. Inter-observer variabilities and variabilities between the algorithm and each observer were quantified by means of the Dice similarity index and a distance measure. Inter-observer variabilities and variabilities between observers and algorithm were found to be comparable, suggesting that the proposed algorithm is adequate for PET/MR co-segmentation. Moreover, taking into account combined PET/MR data resulted in more consistent tumor delineations compared to MR information only.

  14. Statistical Validation of Brain Tumor Shape Approximation via Spherical Harmonics for Image-Guided Neurosurgery1

    PubMed Central

    Goldberg-Zimring, Daniel; Talos, Ion-Florin; Bhagwat, Jui G.; Haker, Steven J.; Black, Peter M.; Zou, Kelly H.

    2005-01-01

    Rationale and Objectives Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. Materials and Methods Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. Results Tumor volume range was 22413–85189 mm3, and range of number of vertices in triangulated models was 3674–6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 × 10−5 mm−1. Summary measures yielded a DSC range of 0.89–0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002–0.0028 (pooled median, 0.0005). Conclusion 3D shapes of tumors may be approximated by using SH for neurosurgical applications. PMID:15831419

  15. Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

    PubMed Central

    Cheng, Jun; Huang, Wei; Cao, Shuangliang; Yang, Ru; Yang, Wei; Yun, Zhaoqiang; Wang, Zhijian; Feng, Qianjin

    2015-01-01

    Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI. PMID:26447861

  16. Implementation of talairach atlas based automated brain segmentation for radiation therapy dosimetry.

    PubMed

    Popple, R A; Griffith, H R; Sawrie, S M; Fiveash, J B; Brezovich, I A

    2006-02-01

    Radiotherapy for brain cancer inevitably results in irradiation of uninvolved brain. While it has been demonstrated that irradiation of the brain can result in cognitive deficits, dose-volume relationships are not well established. There is little work correlating a particular cognitive deficit with dose received by the region of the brain responsible for the specific cognitive function. One obstacle to such studies is that identification of brain anatomy is both labor intensive and dependent on the individual performing the segmentation. Automatic segmentation has the potential to be both efficient and consistent. Brains2 is a software package developed by the University of Iowa for MRI volumetric studies. It utilizes MR images, the Talairach atlas, and an artificial neural network (ANN) to segment brain images into substructures in a standardized manner. We have developed a software package, Brains2DICOM, that converts the regions of interest identified by Brains2 into a DICOM radiotherapy structure set. The structure set can be imported into a treatment planning system for dosimetry. We demonstrated the utility of Brains2DICOM using a test case, a 34-year-old man with diffuse astrocytoma treated with three-dimensional conformal radiotherapy. Brains2 successfully applied the Talairach atlas to identify the right and left frontal, parietal, temporal, occipital, subcortical, and cerebellum regions. Brains2 was not successful in applying the ANN to identify small structures, such as the hippocampus and caudate. Further work is necessary to revise the ANN or to develop new methods for identification of small structures in the presence of disease and radiation induced changes. The segmented regions-of-interest were transferred to our commercial treatment planning system using DICOM and dose-volume histograms were constructed. This method will facilitate the acquisition of data necessary for the development of normal tissue complication probability (NTCP) models that

  17. A Brain Tumor/Organotypic Slice Co-culture System for Studying Tumor Microenvironment and Targeted Drug Therapies.

    PubMed

    Chadwick, Emily J; Yang, David P; Filbin, Mariella G; Mazzola, Emanuele; Sun, Yu; Behar, Oded; Pazyra-Murphy, Maria F; Goumnerova, Liliana; Ligon, Keith L; Stiles, Charles D; Segal, Rosalind A

    2015-11-07

    Brain tumors are a major cause of cancer-related morbidity and mortality. Developing new therapeutics for these cancers is difficult, as many of these tumors are not easily grown in standard culture conditions. Neurosphere cultures under serum-free conditions and orthotopic xenografts have expanded the range of tumors that can be maintained. However, many types of brain tumors remain difficult to propagate or study. This is particularly true for pediatric brain tumors such as pilocytic astrocytomas and medulloblastomas. This protocol describes a system that allows primary human brain tumors to be grown in culture. This quantitative assay can be used to investigate the effect of microenvironment on tumor growth, and to test new drug therapies. This protocol describes a system where fluorescently labeled brain tumor cells are grown on an organotypic brain slice from a juvenile mouse. The response of tumor cells to drug treatments can be studied in this assay, by analyzing changes in the number of cells on the slice over time. In addition, this system can address the nature of the microenvironment that normally fosters growth of brain tumors. This brain tumor organotypic slice co-culture assay provides a propitious system for testing new drugs on human tumor cells within a brain microenvironment.

  18. Development of multifunctional nanoparticles for brain tumor diagnosis and therapy

    NASA Astrophysics Data System (ADS)

    Veiseh, Omid

    Magnetic nanoparticles (MNPs) represent a class of non-invasive imaging agents developed for magnetic resonance (MR) imaging and drug delivery. MNPs have traditionally been developed for disease imaging via passive targeting, but recent advances in nanotechnology have enabled cellular-specific targeting, drug delivery and multi-modal imaging using these nanoparticles. Opportunities now exist to engineer MNP with designated features (e.g., size, coatings, and molecular functionalizations) for specific biomedical applications. The goal of this interdisciplinary research project is to develop targeting multifunctional nanoparticles, serving as both contrast agents and drug carriers that can effectively pass biological barriers, for diagnosis, staging and treatment of brain tumors. The developed nanoparticle system consists of a superparamagnetic iron oxide nanoparticle core (NP) and a shell comprised of biodegradable polymers such as polyethylene glycol (PEG) and chitosan. Additionally, near-infrared fluorescing (NIRF) molecules were integrated onto the NP shell to enable optical detection. Tumor targeting was achieved by the addition of chlorotoxin, a peptide with that has high affinity to 74 out of the 79 classifications of primary brain tumors and ability to illicit a therapeutic effect. This novel NP system was tested both in vitro and in vivo and was shown to specifically target gliomas in tissue culture and medulloblastomas in transgenic mice with an intact blood brain barriers (BBB), and delineate tumor boundaries in both MR and optical imaging. Additionally, the therapeutic potential of this NP system was explored in vitro, which revealed a unique nanoparticle-enabled pathway that enhances the therapeutic potential of bound peptides by promoting the internalization of membrane bound cell surface receptors. This NP system was further modified with siRNA and evaluated as a carrier for brain tumor targeted gene therapy. Most significantly, the evaluation of

  19. Optimal Co-segmentation of Tumor in PET-CT Images with Context Information

    PubMed Central

    Song, Qi; Bai, Junjie; Han, Dongfeng; Bhatia, Sudershan; Sun, Wenqing; Rockey, William; Bayouth, John E.; Buatti, John M.

    2014-01-01

    PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT. PMID:23693127

  20. American brain tumor patients treated with BNCT in Japan

    SciTech Connect

    Laramore, G.E.; Griffin, B.R.; Spence, A.

    1995-11-01

    The purpose of this work is to establish and maintain a database for patients from the United States who have received BNCT in Japan for malignant gliomas of the brain. This database will serve as a resource for the DOE to aid in decisions relating to BNCT research in the United States, as well as assisting the design and implementation of clinical trials of BNCT for brain cancer patients in this country. The database will also serve as an information resource for patients with brain tumors and their families who are considering this form of therapy.

  1. Epigenetics in Brain Tumors: HDACs Take Center Stage

    PubMed Central

    Eyüpoglu, Ilker Y.; Savaskan, Nicolai E.

    2016-01-01

    Primary tumors of the brain account for 2 % of all cancers with malignant gliomas taking the lion’s share at 70 %. Malignant gliomas (high grade gliomas WHO° III and °IV) belong to one of the most threatening tumor entities known for their disappointingly short median survival time of just 14 months despite maximum therapy according to current gold standards. Malignant gliomas manifest various factors, through which they adapt and manipulate the tumor microenvironment to their advantage. Epigenetic mechanisms operate on the tumor microenvironment by de- and methylation processes and imbalances between the histone deacetylases (HDAC) and histone acetylases (HAT). Many compounds have been discovered modulating epigenetically controlled signals. Recent studies indicate that xCT (system xc-, SLC7a11) and CD44 (H-CAM, ECM-III, HUTCH-1) functions as a bridge between these epigenetic regulatory mechanisms and malignant glioma progression. The question that ensues is the extent to which therapeutic intervention on these signaling pathways would exert influence on the treatment of malignant gliomas as well as the extent to which manipulation of HDAC activity can sensitize tumor cells for chemotherapeutics through ‘epigenetic priming’. In light of considering the current stagnation in the development of therapeutic options, the need for new strategies in the treatment of gliomas has never been so pressing. In this context the possibility of pharmacological intervention on tumor-associated genes by epigenetic priming opens a novel path in the treatment of primary brain tumors. PMID:26521944

  2. Metastatic cardiac tumor manifested by persistent ST-segment elevation with coexisting reciprocal changes on electrocardiography.

    PubMed

    Cheon, Dae Young; Park, Kyoung-Ha; Hong, Seong Eun; Lee, Soo Haeng; Jang, Seung Hun; Park, Woo Jung

    2014-01-01

    In cases with metastatic invasion of the heart, electrocardiographic abnormalities are commonly seen. However, most of these electrocardiographic changes are nonspecific; certain findings may be highly suggestive of myocardial involvement of the tumor. We report a patient with lung cancer who presented with persistent ST-segment elevation with coexisting reciprocal changes on electrocardiography due to myocardial invasion of the lung cancer.

  3. Genetic abnormality predicts benefit for a rare brain tumor

    Cancer.gov

    A clinical trial has shown that addition of chemotherapy to radiation therapy leads to a near doubling of median survival time in patients with a form of brain tumor (oligodendroglioma) that carries a chromosomal abnormality called the 1p19q co-deletion.

  4. Survival Rates for Selected Childhood Brain and Spinal Cord Tumors

    MedlinePlus

    ... are at best rough estimates. Your child’s doctor knows your child’s situation and is your best source of information on this topic. Last Medical Review: 08/12/2014 Last Revised: 01/21/2016 Back to top » Guide Topics What Is Brain/CNS Tumors In Children? Causes, Risk Factors, and ...

  5. Learning Profiles of Survivors of Pediatric Brain Tumors

    ERIC Educational Resources Information Center

    Barkon, Beverly

    2009-01-01

    By 2010 it is predicted that one in 900 adults will be survivors of some form of pediatric cancer. The numbers are somewhat lower for survivors of brain tumors, though their numbers are increasing. Schools mistakenly believe that these children easily fit pre-existing categories of disability. Though these students share some of the…

  6. Life satisfaction in adult survivors of childhood brain tumors.

    PubMed

    Crom, Deborah B; Li, Zhenghong; Brinkman, Tara M; Hudson, Melissa M; Armstrong, Gregory T; Neglia, Joseph; Ness, Kirsten K

    2014-01-01

    Adult survivors of childhood brain tumors experience multiple, significant, lifelong deficits as a consequence of their malignancy and therapy. Current survivorship literature documents the substantial impact such impairments have on survivors' physical health and quality of life. Psychosocial reports detail educational, cognitive, and emotional limitations characterizing survivors as especially fragile, often incompetent, and unreliable in evaluating their circumstances. Anecdotal data suggest some survivors report life experiences similar to those of healthy controls. The aim of our investigation was to determine whether life satisfaction in adult survivors of childhood brain tumors differs from that of healthy controls and to identify potential predictors of life satisfaction in survivors. This cross-sectional study compared 78 brain tumor survivors with population-based matched controls. Chi-square tests, t tests, and linear regression models were used to investigate patterns of life satisfaction and identify potential correlates. Results indicated that life satisfaction of adult survivors of childhood brain tumors was similar to that of healthy controls. Survivors' general health expectations emerged as the primary correlate of life satisfaction. Understanding life satisfaction as an important variable will optimize the design of strategies to enhance participation in follow-up care, reduce suffering, and optimize quality of life in this vulnerable population.

  7. A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

    PubMed

    Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

    2015-01-01

    PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies. PMID:26736741

  8. A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

    PubMed

    Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

    2015-01-01

    PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.

  9. Gene Therapy for Brain Tumors: Basic Developments and Clinical Implementation

    PubMed Central

    Assi, Hikmat; Candolfi, Marianela; Baker, Gregory; Mineharu, Yohei; Lowenstein, Pedro R; Castro, Maria G

    2012-01-01

    Glioblastoma multiforme (GBM) is the most common and deadliest of adult primary brain tumors. Due to its invasive nature and sensitive location, complete resection remains virtually impossible. The resistance of GBM against chemotherapy and radiotherapy necessitate the development of novel therapies. Gene therapy is proposed for the treatment of brain tumors and has demonstrated pre-clinical efficacy in animal models. Here we review the various experimental therapies that have been developed for GBM including both cytotoxic and immune stimulatory approaches. We also review the combined conditional cytotoxic immune stimulatory therapy that our lab has developed which is dependent on the adenovirus mediated expression of the conditional cytotoxic gene, Herpes Simplex Type 1 Thymidine Kinase (TK) and the powerful DC growth factor Fms-like tyrosine kinase 3 ligand (Flt3L). Combined delivery of these vectors elicits tumor cell death and an anti-tumor adaptive immune response that requires TLR2 activation. The implications of our studies indicate that the combined cytotoxic and immunotherapeutic strategies are effective strategies to combat deadly brain tumors and warrant their implementation in human Phase I clinical trials for GBM. PMID:22906921

  10. Quantitative assessment of MS plaques and brain atrophy in multiple sclerosis using semiautomatic segmentation method

    NASA Astrophysics Data System (ADS)

    Heinonen, Tomi; Dastidar, Prasun; Ryymin, Pertti; Lahtinen, Antti J.; Eskola, Hannu; Malmivuo, Jaakko

    1997-05-01

    Quantitative magnetic resonance (MR) imaging of the brain is useful in multiple sclerosis (MS) in order to obtain reliable indices of disease progression. The goal of this project was to estimate the total volume of gliotic and non gliotic plaques in chronic progressive multiple sclerosis with the help of a semiautomatic segmentation method developed at the Ragnar Granit Institute. Youth developed program running on a PC based computer provides de displays of the segmented data, in addition to the volumetric analyses. The volumetric accuracy of the program was demonstrated by segmenting MR images of fluid filed syringes. An anatomical atlas is to be incorporated in the segmentation system to estimate the distribution of MS plaques in various neural pathways of the brain. A total package including MS plaque volume estimation, estimation of brain atrophy and ventricular enlargement, distribution of MS plaques in different neural segments of the brain has ben planned for the near future. Our study confirmed that total lesion volumes in chronic MS disease show a poor correlation to EDSS scores but show a positive correlation to neuropsychological scores. Therefore accurate total volume measurements of MS plaques using the developed semiautomatic segmentation technique helped us to evaluate the degree of neuropsychological impairment.

  11. Brain MRI segmentation and lesion detection using generalized Gaussian and Rician modeling

    NASA Astrophysics Data System (ADS)

    Wu, Xuqiang; Bricq, Stéphanie; Collet, Christophe

    2011-03-01

    In this paper we propose a mixed noise modeling so as to segment the brain and to detect lesion. Indeed, accurate segmentation of multimodal (T1, T2 and Flair) brain MR images is of great interest for many brain disorders but requires to efficiently manage multivariate correlated noise between available modalities. We addressed this problem in1 by proposing an entirely unsupervised segmentation scheme, taking into account multivariate Gaussian noise, imaging artifacts,intrinsic tissue variation and partial volume effects in a Bayesian framework. Nevertheless, tissue classification remains a challenging task especially when one addresses the lesion detection during segmentation process2 as we did. In order to improve brain segmentation into White and Gray Matter (resp. WM and GM) and cerebro-spinal fluid (CSF), we propose to fit a Rician (RC) density distribution for CSF whereas Generalized Gaussian (GG) models are used to fit the likelihood between model and data corresponding to WM and GM. In this way, we present in this paper promising results showing that in a multimodal segmentation-detection scheme, this model fits better with the data and increases lesion detection rate. One of the main challenges consists in being able to take into account various pdf (Gaussian and non- Gaussian) for correlated noise between modalities and to show that lesion-detection is then clearly improved, probably because non-Gaussian noise better fits to the physic of MRI image acquisition.

  12. Uniform brain tumor distribution and tumor associated macrophage targeting of systemically administered dendrimers

    PubMed Central

    Zhang, Fan; Mastorakos, Panagiotis; Mishra, Manoj K.; Mangraviti, Antonella; Hwang, Lee; Zhou, Jinyuan; Hanes, Justin; Brem, Henry; Olivi, Alessandro; Tyler, Betty; Kannan, Rangaramanujam M.

    2015-01-01

    Effective blood–brain tumor barrier penetration and uniform solid tumor distribution can significantly enhance therapeutic delivery to brain tumors. Hydroxyl-functionalized, generation-4 poly(amidoamine) (PAMAM) dendrimers, with their small size, near-neutral surface charge, and the ability to selectively localize in cells associated with neuroinflammation may offer new opportunities to address these challenges. In this study we characterized the intracranial tumor biodistribution of systemically delivered PAMAM dendrimers in an intracranial rodent gliosarcoma model using fluorescence-based quantification methods and high resolution confocal microscopy. We observed selective and homogeneous distribution of dendrimer throughout the solid tumor (~6 mm) and peritumoral area within fifteen minutes after systemic administration, with subsequent accumulation and retention in tumor associated microglia/macrophages (TAMs). Neuroinflammation and TAMs have important growth promoting and pro-invasive effects in brain tumors. The rapid clearance of systemically administered dendrimers from major organs promises minimal off-target adverse effects of conjugated drugs. Therefore, selective delivery of immunomodulatory molecules to TAM, using hydroxyl PAMAM dendrimers, may hold promise for therapy of glioblastoma. PMID:25818456

  13. Uniform brain tumor distribution and tumor associated macrophage targeting of systemically administered dendrimers.

    PubMed

    Zhang, Fan; Mastorakos, Panagiotis; Mishra, Manoj K; Mangraviti, Antonella; Hwang, Lee; Zhou, Jinyuan; Hanes, Justin; Brem, Henry; Olivi, Alessandro; Tyler, Betty; Kannan, Rangaramanujam M

    2015-06-01

    Effective blood-brain tumor barrier penetration and uniform solid tumor distribution can significantly enhance therapeutic delivery to brain tumors. Hydroxyl-functionalized, generation-4 poly(amidoamine) (PAMAM) dendrimers, with their small size, near-neutral surface charge, and the ability to selectively localize in cells associated with neuroinflammation may offer new opportunities to address these challenges. In this study we characterized the intracranial tumor biodistribution of systemically delivered PAMAM dendrimers in an intracranial rodent gliosarcoma model using fluorescence-based quantification methods and high resolution confocal microscopy. We observed selective and homogeneous distribution of dendrimer throughout the solid tumor (∼6 mm) and peritumoral area within fifteen minutes after systemic administration, with subsequent accumulation and retention in tumor associated microglia/macrophages (TAMs). Neuroinflammation and TAMs have important growth promoting and pro-invasive effects in brain tumors. The rapid clearance of systemically administered dendrimers from major organs promises minimal off-target adverse effects of conjugated drugs. Therefore, selective delivery of immunomodulatory molecules to TAM, using hydroxyl PAMAM dendrimers, may hold promise for therapy of glioblastoma.

  14. Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases

    NASA Astrophysics Data System (ADS)

    Cherel, Marie; Budin, Francois; Prastawa, Marcel; Gerig, Guido; Lee, Kevin; Buss, Claudia; Lyall, Amanda; Zaldarriaga Consing, Kirsten; Styner, Martin

    2015-03-01

    Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule.

  15. Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model

    NASA Astrophysics Data System (ADS)

    Angelini, Elsa D.; Song, Ting; Mensh, Brett D.; Laine, Andrew

    2004-05-01

    Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours.

  16. Active-contour-model-based edge restriction and attraction field regularization for brain MRI segmentation

    NASA Astrophysics Data System (ADS)

    Luan, H.; Qi, Feihu

    2004-11-01

    Constructing 3D models of the object of interest from brain MRI is useful in numerous biomedical imaging application. In general, the construction of the 3D models is generally carried out according to the contours obtained from a 2D segmentation of each MR slice, so the equality of the 3D model strongly depends on the precision of the segmentation process. Active contour model is an effective edge-based method in segmenting an object of interest. However, its application, which segment boundary of anatomical structure of brain MRI, encounters many difficulties due to undesirable properties of brain MRI, for example complex background, intensity inhomogeneity and discontinuous edges. This paper proposes an active contour model to solve the problems of automatically segmenting the object of interest from a brain MRI. In this proposed algorithm, a new method of calculating attraction field has been developed. This method is based on edge restriction and attraction field regularization. Edge restriction introduces prior knowledge about the object of interest to free contours of being affected by edges of other anatomical structures or spurious edges, while attraction field regularization enables our algorithm to extract boundary correctly even at the place, where the edge of object of interest is discontinuous, by diffusing the edge information gotten after edge restriction. When we apply this proposed algorithm to brain MRI, the result shows this proposed algorithm could overcome those difficulties we mentioned above and convergence to object boundary quickly and accurately.

  17. Automatic Tissue Segmentation of Neonate Brain MR Images with Subject-specific Atlases

    PubMed Central

    Cherel, Marie; Budin, Francois; Prastawa, Marcel; Gerig, Guido; Lee, Kevin; Buss, Claudia; Lyall, Amanda; Consing, Kirsten Zaldarriaga; Styner, Martin

    2015-01-01

    Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule. PMID:26089584

  18. Multi-fractal detrended texture feature for brain tumor classification

    NASA Astrophysics Data System (ADS)

    Reza, Syed M. S.; Mays, Randall; Iftekharuddin, Khan M.

    2015-03-01

    We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

  19. Cerenkov and radioluminescence imaging of brain tumor specimens during neurosurgery

    NASA Astrophysics Data System (ADS)

    Spinelli, Antonello Enrico; Schiariti, Marco P.; Grana, Chiara M.; Ferrari, Mahila; Cremonesi, Marta; Boschi, Federico

    2016-05-01

    We presented the first example of Cerenkov luminescence imaging (CLI) and radioluminescence imaging (RLI) of human tumor specimens. A patient with a brain meningioma localized in the left parietal region was injected with 166 MBq of Y90-DOTATOC the day before neurosurgery. The specimens of the tumor removed during surgery were imaged using both CLI and RLI using an optical imager prototype developed in our laboratory. The system is based on a cooled electron multiplied charge coupled device coupled with an f/0.95 17-mm C-mount lens. We showed for the first time the possibility of obtaining CLI and RLI images of fresh human brain tumor specimens removed during neurosurgery.

  20. Cerenkov and radioluminescence imaging of brain tumor specimens during neurosurgery

    NASA Astrophysics Data System (ADS)

    Spinelli, Antonello Enrico; Schiariti, Marco P.; Grana, Chiara M.; Ferrari, Mahila; Cremonesi, Marta; Boschi, Federico

    2016-05-01

    We presented the first example of Cerenkov luminescence imaging (CLI) and radioluminescence imaging (RLI) of human tumor specimens. A patient with a brain meningioma localized in the left parietal region was injected with 166 MBq of Y90-DOTATOC the day before neurosurgery. The specimens of the tumor removed during surgery were imaged using both CLI and RLI using an optical imager prototype developed in our laboratory. The system is based on a cooled electron multiplied charge coupled device coupled with an f/0.95 17-mm C-mount lens. We showed for the first time the possibility of obtaining CLI and RLI images of fresh human brain tumor specimens removed during neurosurgery.

  1. Segmentation of intensity inhomogeneous brain MR images using active contours.

    PubMed

    Akram, Farhan; Kim, Jeong Heon; Lim, Han Ul; Choi, Kwang Nam

    2014-01-01

    Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods. PMID:25143780

  2. Therapeutic Potential of Curcumin for the Treatment of Brain Tumors

    PubMed Central

    Klinger, Neil V.

    2016-01-01

    Brain malignancies currently carry a poor prognosis despite the current multimodal standard of care that includes surgical resection and adjuvant chemotherapy and radiation. As new therapies are desperately needed, naturally occurring chemical compounds have been studied for their potential chemotherapeutic benefits and low toxicity profile. Curcumin, found in the rhizome of turmeric, has extensive therapeutic promise via its antioxidant, anti-inflammatory, and antiproliferative properties. Preclinical in vitro and in vivo data have shown it to be an effective treatment for brain tumors including glioblastoma multiforme. These effects are potentiated by curcumin's ability to induce G2/M cell cycle arrest, activation of apoptotic pathways, induction of autophagy, disruption of molecular signaling, inhibition of invasion, and metastasis and by increasing the efficacy of existing chemotherapeutics. Further, clinical data suggest that it has low toxicity in humans even at large doses. Curcumin is a promising nutraceutical compound that should be evaluated in clinical trials for the treatment of human brain tumors. PMID:27807473

  3. Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer's dementia or mild cognitive impairment.

    PubMed

    Fellhauer, Iven; Zöllner, Frank G; Schröder, Johannes; Degen, Christina; Kong, Li; Essig, Marco; Thomann, Philipp A; Schad, Lothar R

    2015-09-30

    Magnetic resonance imaging (MRI) and brain volumetry allow for the quantification of changes in brain volume using automatic algorithms which are widely used in both, clinical and scientific studies. However, studies comparing the reliability of these programmes are scarce and mainly involved MRI derived from younger healthy controls. This study evaluates the reliability of frequently used segmentation programmes (SPM, FreeSurfer, FSL) using a realistic digital brain phantom and MRI brain acquisitions from patients with manifest Alzheimer's disease (AD, n=34), mild cognitive impairment (MCI, n=60), and healthy subjects (n=32) matched for age and sex. Analysis of the brain phantom dataset demonstrated that SPM, FSL and FreeSurfer underestimate grey matter and overestimate white matter volumes with increasing noise. FreeSurfer calculated overall smaller brain volumes with increasing noise. Image inhomogeneity had only minor, non- significant effects on the results obtained with SPM and FreeSurfer 5.1, but had effects on the FSL results (increased white matter volumes with decreased grey matter volumes). The analysis of the patient data yielded decreasing volumes of grey and white matter with progression of brain atrophy independent of the method used. FreeSurfer calculated the largest grey matter and the smallest white matter volumes. FSL calculated the smallest grey matter volumes; SPM the largest white matter volumes. Best results are obtained with good image quality. With poor image quality, especially noise, SPM provides the best segmentation results. An optimised template for segmentation had no significant effect on segmentation results. While our findings underline the applicability of the programmes investigated, SPM may be the programme of choice when MRIs with limited image quality or brain images of elderly should be analysed. PMID:26211622

  4. Automated fetal brain segmentation from 2D MRI slices for motion correction.

    PubMed

    Keraudren, K; Kuklisova-Murgasova, M; Kyriakopoulou, V; Malamateniou, C; Rutherford, M A; Kainz, B; Hajnal, J V; Rueckert, D

    2014-11-01

    Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.

  5. Clinical considerations for neutron capture therapy of brain tumors

    SciTech Connect

    Madoc-Jones, H.; Wazer, D.E.; Zamenhof, R.G.; Harling, O.K.; Bernard, J.A. Jr. )

    1990-01-01

    The radiotherapeutic management of primary brain tumors and metastatic melanoma in brain has had disappointing clinical results for many years. Although neutron capture therapy was tried in the United States in the 1950s and 1960s, the results were not as hoped. However, with the newly developed capability to measure boron concentrations in blood and tissue both quickly and accurately, and with the advent of epithermal neutron beams obviating the need for scalp and skull reflection, it should now be possible to mount such a clinical trial of NCT again and avoid serious complications. As a prerequisite, it will be important to demonstrate the differential uptake of boron compound in brain tumor as compared with normal brain and its blood supply. If this can be done, then a trial of boron neutron capture therapy for brain tumors should be feasible. Because boronated phenylalanine has been demonstrated to be preferentially taken up by melanoma cells through the biosynthetic pathway for melanin, there is special interest in a trial of boron neutron capture therapy for metastatic melanoma in brain. Again, the use of an epithermal beam would make this a practical possibility. However, because any epithermal (or thermal) beam must contain a certain contaminating level of gamma rays, and because even a pure neutron beam causes gamma rays to be generated when it interacts with tissue, we think that it is essential to deliver treatments with an epithermal beam for boron neutron capture therapy in fractions in order to minimize the late-effects of low-LET gamma rays in the normal tissue. I look forward to the remainder of this Workshop, which will detail recent progress in the development of epithermal, as well as thermal, beams and new methods for tracking and measuring the uptake of boron in normal and tumor tissues. 10 references.

  6. Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline.

    PubMed

    Wang, Jiahui; Vachet, Clement; Rumple, Ashley; Gouttard, Sylvain; Ouziel, Clémentine; Perrot, Emilie; Du, Guangwei; Huang, Xuemei; Gerig, Guido; Styner, Martin

    2014-01-01

    Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures. PMID

  7. Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

    PubMed Central

    Wang, Jiahui; Vachet, Clement; Rumple, Ashley; Gouttard, Sylvain; Ouziel, Clémentine; Perrot, Emilie; Du, Guangwei; Huang, Xuemei; Gerig, Guido; Styner, Martin

    2014-01-01

    Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures

  8. Why does Jack, and not Jill, break his crown? Sex disparity in brain tumors

    PubMed Central

    2012-01-01

    It is often reported that brain tumors occur more frequently in males, and that males suffer a worse outcome from brain tumors than females. If correct, these observations suggest that sex plays a fundamental role in brain tumor biology. The following review of the literature regarding primary and metastatic brain tumors, reveals that brain tumors do occur more frequently in males compared to females regardless of age, tumor histology, or region of the world. Sexually dimorphic mechanisms that might control tumor cell biology, as well as immune and brain microenvironmental responses to cancer, are explored as the basis for this sex disparity. Elucidating the mechanisms by which sex chromosomes and sex hormones impact on brain tumorigenesis and progression will advance our understanding of basic cancer biology and is likely to be essential for optimizing the care of brain tumor patients. PMID:22277186

  9. Banking Brain Tumor Specimens Using a University Core Facility.

    PubMed

    Bregy, Amade; Papadimitriou, Kyriakos; Faber, David A; Shah, Ashish H; Gomez, Carmen R; Komotar, Ricardo J; Egea, Sophie C

    2015-08-01

    Within the past three decades, the significance of banking human cancer tissue for the advancement of cancer research has grown exponentially. The purpose of this article is to detail our experience in collecting brain tumor specimens in collaboration with the University of Miami/Sylvester Tissue Bank Core Facility (UM-TBCF), to ensure the availability of high-quality samples of central nervous system tumor tissue for research. Successful tissue collection begins with obtaining informed consent from patients following institutional IRB and federal HIPAA guidelines, and it needs a well-trained professional staff and continued maintenance of high ethical standards and record keeping. Since starting in 2011, we have successfully banked 225 brain tumor specimens for research. Thus far, the most common tumor histology identified among those specimens has been glioblastoma (22.1%), followed by meningioma (18.1%). The majority of patients were White, non-Hispanics accounting for 45.1% of the patient population; Hispanic/Latinos accounted for 23%, and Black/African Americans accounted for 14%, which represent the particular population of the State of Florida according to the 2010 census data. The most common tumors found in each subgroup were as follows: Black/African American, glioblastoma and meningioma; Hispanic, metastasis and glioblastoma; White, glioblastoma and meningioma. The UM-TBCF is a valuable repository, offering high-quality tumor samples from a unique patient population. PMID:26280502

  10. Mitochondrial control by DRP1 in brain tumor initiating cells.

    PubMed

    Xie, Qi; Wu, Qiulian; Horbinski, Craig M; Flavahan, William A; Yang, Kailin; Zhou, Wenchao; Dombrowski, Stephen M; Huang, Zhi; Fang, Xiaoguang; Shi, Yu; Ferguson, Ashley N; Kashatus, David F; Bao, Shideng; Rich, Jeremy N

    2015-04-01

    Brain tumor initiating cells (BTICs) co-opt the neuronal high affinity glucose transporter, GLUT3, to withstand metabolic stress. We investigated another mechanism critical to brain metabolism, mitochondrial morphology, in BTICs. BTIC mitochondria were fragmented relative to non-BTIC tumor cell mitochondria, suggesting that BTICs increase mitochondrial fission. The essential mediator of mitochondrial fission, dynamin-related protein 1 (DRP1), showed activating phosphorylation in BTICs and inhibitory phosphorylation in non-BTIC tumor cells. Targeting DRP1 using RNA interference or pharmacologic inhibition induced BTIC apoptosis and inhibited tumor growth. Downstream, DRP1 activity regulated the essential metabolic stress sensor, AMP-activated protein kinase (AMPK), and targeting AMPK rescued the effects of DRP1 disruption. Cyclin-dependent kinase 5 (CDK5) phosphorylated DRP1 to increase its activity in BTICs, whereas Ca(2+)-calmodulin-dependent protein kinase 2 (CAMK2) inhibited DRP1 in non-BTIC tumor cells, suggesting that tumor cell differentiation induces a regulatory switch in mitochondrial morphology. DRP1 activation correlated with poor prognosis in glioblastoma, suggesting that mitochondrial dynamics may represent a therapeutic target for BTICs. PMID:25730670

  11. Banking Brain Tumor Specimens Using a University Core Facility.

    PubMed

    Bregy, Amade; Papadimitriou, Kyriakos; Faber, David A; Shah, Ashish H; Gomez, Carmen R; Komotar, Ricardo J; Egea, Sophie C

    2015-08-01

    Within the past three decades, the significance of banking human cancer tissue for the advancement of cancer research has grown exponentially. The purpose of this article is to detail our experience in collecting brain tumor specimens in collaboration with the University of Miami/Sylvester Tissue Bank Core Facility (UM-TBCF), to ensure the availability of high-quality samples of central nervous system tumor tissue for research. Successful tissue collection begins with obtaining informed consent from patients following institutional IRB and federal HIPAA guidelines, and it needs a well-trained professional staff and continued maintenance of high ethical standards and record keeping. Since starting in 2011, we have successfully banked 225 brain tumor specimens for research. Thus far, the most common tumor histology identified among those specimens has been glioblastoma (22.1%), followed by meningioma (18.1%). The majority of patients were White, non-Hispanics accounting for 45.1% of the patient population; Hispanic/Latinos accounted for 23%, and Black/African Americans accounted for 14%, which represent the particular population of the State of Florida according to the 2010 census data. The most common tumors found in each subgroup were as follows: Black/African American, glioblastoma and meningioma; Hispanic, metastasis and glioblastoma; White, glioblastoma and meningioma. The UM-TBCF is a valuable repository, offering high-quality tumor samples from a unique patient population.

  12. Radiation treatment of brain tumors: Concepts and strategies

    SciTech Connect

    Marks, J.E. )

    1989-01-01

    Ionizing radiation has demonstrated clinical value for a multitude of CNS tumors. Application of the different physical modalities available has made it possible for the radiotherapist to concentrate the radiation in the region of the tumor with relative sparing of the surrounding normal tissues. Correlation of radiation dose with effect on cranial soft tissues, normal brain, and tumor has shown increasing effect with increasing dose. By using different physical modalities to alter the distribution of radiation dose, it is possible to increase the dose to the tumor and reduce the dose to the normal tissues. Alteration of the volume irradiated and the dose delivered to cranial soft tissues, normal brain, and tumor are strategies that have been effective in improving survival and decreasing complications. The quest for therapeutic gain using hyperbaric oxygen, neutrons, radiation sensitizers, chemotherapeutic agents, and BNCT has met with limited success. Both neoplastic and normal cells are affected simultaneously by all modalities of treatment, including ionizing radiation. Consequently, one is unable to totally depopulate a tumor without irreversibly damaging the normal tissues. In the case of radiation, it is the brain that limits delivery of curative doses, and in the case of chemical additives, it is other organ systems, such as bone marrow, liver, lung, kidneys, and peripheral nerves. Thus, the major obstacle in the treatment of malignant gliomas is our inability to preferentially affect the tumor with the modalities available. Until it is possible to directly target the neoplastic cell without affecting so many of the adjacent normal cells, the quest for therapeutic gain will go unrealized.72 references.

  13. Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST).

    PubMed

    Amann, Michael; Andělová, Michaela; Pfister, Armanda; Mueller-Lenke, Nicole; Traud, Stefan; Reinhardt, Julia; Magon, Stefano; Bendfeldt, Kerstin; Kappos, Ludwig; Radue, Ernst-Wilhelm; Stippich, Christoph; Sprenger, Till

    2015-01-01

    Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and -25.2% (nucleus accumbens). The respective values for the BG were -2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D

  14. Brain tumors and synchrotron radiation: Methodological developments in quantitative brain perfusion imaging and radiation therapy

    SciTech Connect

    Adam, Jean-Francois

    2005-04-01

    High-grade gliomas are the most frequent type of primary brain tumors in adults. Unfortunately, the management of glioblastomas is still mainly palliative and remains a difficult challenge, despite advances in brain tumor molecular biology and in some emerging therapies. Synchrotron radiation opens fields for medical imaging and radiation therapy by using monochromatic intense x-ray beams. It is now well known that angiogenesis plays a critical role in the tumor growth process and that brain perfusion is representative of the tumor mitotic activity. Synchrotron radiation quantitative computed tomography (SRCT) is one of the most accurate techniques for measuring in vivo contrast agent concentration and thus computing precise and accurate absolute values of the brain perfusion key parameters. The methodological developments of SRCT absolute brain perfusion measurements as well as their preclinical validation are detailed in this thesis. In particular, absolute cerebral volume and blood brain barrier permeability high-resolution (pixel size <50x50 {mu}m{sup 2}) parametric maps were reported. In conventional radiotherapy, the treatment of these tumors remains a delicate challenge, because the damages to the surrounding normal brain tissue limit the amount of radiation that can be delivered. One strategy to overcome this limitation is to infuse an iodinated contrast agent to the patient during the irradiation. The contrast agent accumulates in the tumor, through the broken blood brain barrier, and the irradiation is performed with kilovoltage x rays, in tomography mode, the tumor being located at the center of rotation and the beam size adjusted to the tumor dimensions. The dose enhancement results from the photoelectric effect on the heavy element and from the irradiation geometry. Synchrotron beams, providing high intensity, tunable monochromatic x rays, are ideal for this treatment. The beam properties allow the selection of monochromatic irradiation, at the optimal

  15. Early Expansion of the Intracranial CSF Volume After Palliative Whole-Brain Radiotherapy: Results of a Longitudinal CT Segmentation Analysis

    SciTech Connect

    Sanghera, Paul; Gardner, Sandra L.; Scora, Daryl; Davey, Phillip

    2010-03-15

    Purpose: To assess cerebral atrophy after radiotherapy, we measured intracranial cerebrospinal fluid volume (ICSFV) over time after whole-brain radiotherapy (WBRT) and compared it with published normal-population data. Methods and Materials: We identified 9 patients receiving a single course of WBRT (30 Gy in 10 fractions over 2 weeks) for ipsilateral brain metastases with at least 3 years of computed tomography follow-up. Segmentation analysis was confined to the tumor-free hemi-cranium. The technique was semiautomated by use of thresholds based on scanned image intensity. The ICSFV percentage (ratio of ICSFV to brain volume) was used for modeling purposes. Published normal-population ICSFV percentages as a function of age were used as a control. A repeated-measures model with cross-sectional (between individuals) and longitudinal (within individuals) quadratic components was fitted to the collected data. The influence of clinical factors including the use of subependymal plate shielding was studied. Results: The median imaging follow-up was 6.25 years. There was an immediate increase (p < 0.0001) in ICSFV percentage, which decelerated over time. The clinical factors studied had no significant effect on the model. Conclusions: WBRT immediately accelerates the rate of brain atrophy. This longitudinal study in patients with brain metastases provides a baseline against which the potential benefits of more localized radiotherapeutic techniques such as radiosurgery may be compared.

  16. Emerging techniques and technologies in brain tumor imaging.

    PubMed

    Ellingson, Benjamin M; Bendszus, Martin; Sorensen, A Gregory; Pope, Whitney B

    2014-10-01

    The purpose of this report is to describe the state of imaging techniques and technologies for detecting response of brain tumors to treatment in the setting of multicenter clinical trials. Within currently used technologies, implementation of standardized image acquisition and the use of volumetric estimates and subtraction maps are likely to help to improve tumor visualization, delineation, and quantification. Upon further development, refinement, and standardization, imaging technologies such as diffusion and perfusion MRI and amino acid PET may contribute to the detection of tumor response to treatment, particularly in specific treatment settings. Over the next few years, new technologies such as 2(3)Na MRI and CEST imaging technologies will be explored for their use in expanding the ability to quantitatively image tumor response to therapies in a clinical trial setting.

  17. Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

    PubMed

    Liyanage, Kishan Andre; Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O'Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

    2016-01-01

    Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access. PMID:27285947

  18. Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

    PubMed

    Liyanage, Kishan Andre; Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O'Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

    2016-01-01

    Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

  19. Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation

    PubMed Central

    Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O’Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

    2016-01-01

    Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson’s disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5–0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0–0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access. PMID:27285947

  20. A prior feature SVM-MRF based method for mouse brain segmentation.

    PubMed

    Wu, Teresa; Bae, Min Hyeok; Zhang, Min; Pan, Rong; Badea, Alexandra

    2012-02-01

    We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.

  1. FULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION

    PubMed Central

    Nie, Dong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    2016-01-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.

  2. Sunitinib impedes brain tumor progression and reduces tumor-induced neurodegeneration in the microenvironment

    PubMed Central

    Hatipoglu, Gökçe; Hock, Stefan W; Weiss, Ruth; Fan, Zheng; Sehm, Tina; Ghoochani, Ali; Buchfelder, Michael; Savaskan, Nicolai E; Eyüpoglu, Ilker Y

    2015-01-01

    the brain tumor microenvironment, revealing novel aspects for adjuvant approaches and new clinical assessment criteria when applied to brain tumor patients. PMID:25458015

  3. Optical spectroscopy for stereotactic biopsy of brain tumors

    NASA Astrophysics Data System (ADS)

    Markwardt, Niklas; von Berg, Anna; Fiedler, Sebastian; Goetz, Marcus; Haj-Hosseini, Neda; Polzer, Christoph; Stepp, Herbert; Zelenkov, Petr; Rühm, Adrian

    2015-07-01

    Stereotactic biopsy procedure is performed to obtain a tissue sample for diagnosis purposes. Currently, a fiber-based mechano-optical device for stereotactic biopsies of brain tumors is developed. Two different fluorophores are employed to improve the safety and reliability of this procedure: The fluorescence of intravenously applied indocyanine green (ICG) facilitates the recognition of blood vessels and thus helps minimize the risk of cerebral hemorrhages. 5- aminolevulinic-acid-induced protoporphyrin IX (PpIX) fluorescence is used to localize vital tumor tissue. ICG fluorescence detection using a 2-fiber probe turned out to be an applicable method to recognize blood vessels about 1.5 mm ahead of the fiber tip during a brain tumor biopsy. Moreover, the suitability of two different PpIX excitation wavelengths regarding practical aspects was investigated: While PpIX excitation in the violet region (at 405 nm) allows for higher sensitivity, red excitation (at 633 nm) is noticeably superior with regard to blood layers obscuring the fluorescence signal. Contact measurements on brain simulating agar phantoms demonstrated that a typical blood coverage of the tumor reduces the PpIX signal to about 75% and nearly 0% for 633 nm and 405 nm excitation, respectively. As a result, 633 nm seems to be the wavelength of choice for PpIX-assisted detection of high-grade gliomas in stereotactic biopsy.

  4. Pros and cons of current brain tumor imaging

    PubMed Central

    Ellingson, Benjamin M.; Wen, Patrick Y.; van den Bent, Martin J.; Cloughesy, Timothy F.

    2014-01-01

    Over the past 20 years, very few agents have been approved for the treatment of brain tumors. Recent studies have highlighted some of the challenges in assessing activity in novel agents for the treatment of brain tumors. This paper reviews some of the key challenges related to assessment of tumor response to therapy in adult high-grade gliomas and discusses the strengths and limitations of imaging-based endpoints. Although overall survival is considered the “gold standard” endpoint in the field of oncology, progression-free survival and response rate are endpoints that hold great value in neuro-oncology. Particular focus is given to advancements made since the January 2006 Brain Tumor Endpoints Workshop, including the development of Response Assessment in Neuro-Oncology criteria, the value of T2/fluid-attenuated inversion recovery, use of objective response rates and progression-free survival in clinical trials, and the evaluation of pseudoprogression, pseudoresponse, and inflammatory response in radiographic images. PMID:25313235

  5. SU-E-J-224: Multimodality Segmentation of Head and Neck Tumors

    SciTech Connect

    Aristophanous, M; Yang, J; Beadle, B

    2014-06-01

    Purpose: Develop an algorithm that is able to automatically segment tumor volume in Head and Neck cancer by integrating information from CT, PET and MR imaging simultaneously. Methods: Twenty three patients that were recruited under an adaptive radiotherapy protocol had MR, CT and PET/CT scans within 2 months prior to start of radiotherapy. The patients had unresectable disease and were treated either with chemoradiotherapy or radiation therapy alone. Using the Velocity software, the PET/CT and MR (T1 weighted+contrast) scans were registered to the planning CT using deformable and rigid registration respectively. The PET and MR images were then resampled according to the registration to match the planning CT. The resampled images, together with the planning CT, were fed into a multi-channel segmentation algorithm, which is based on Gaussian mixture models and solved with the expectation-maximization algorithm and Markov random fields. A rectangular region of interest (ROI) was manually placed to identify the tumor area and facilitate the segmentation process. The auto-segmented tumor contours were compared with the gross tumor volume (GTV) manually defined by the physician. The volume difference and Dice similarity coefficient (DSC) between the manual and autosegmented GTV contours were calculated as the quantitative evaluation metrics. Results: The multimodality segmentation algorithm was applied to all 23 patients. The volumes of the auto-segmented GTV ranged from 18.4cc to 32.8cc. The average (range) volume difference between the manual and auto-segmented GTV was −42% (−32.8%–63.8%). The average DSC value was 0.62, ranging from 0.39 to 0.78. Conclusion: An algorithm for the automated definition of tumor volume using multiple imaging modalities simultaneously was successfully developed and implemented for Head and Neck cancer. This development along with more accurate registration algorithms can aid physicians in the efforts to interpret the multitude of

  6. [Image segmentation in tumor CT based on the improved C-V model].

    PubMed

    Zhang, Jianguo; Zhang, Rongguo; Xue, Fei; Liu, Kun

    2012-04-01

    Aiming at the shortcomings of slow convergence and inaccuracy segmentation in non-homogeneous images, improvements were made on the traditional C-V model in two aspects. Firstly, using a novel model based on local gradient, the initial contour of the C-V model was quickly moved near the target border, greatly reducing the evolution time. Secondly, combining the characteristics of GVF model from two directions to the target border, an adaptive velocity reconciling item was added for velocity equation of the C-V model to make the model converge to the true border. The segmentation experiments for liver tumors in CT showed that the proposed method could be effective.

  7. Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

    PubMed Central

    Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex

    2012-01-01

    Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described. PMID:22479421

  8. Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

    NASA Astrophysics Data System (ADS)

    Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Rueckert, Daniel; Aljabar, Paul; Hajnal, Joseph V.; Hammers, Alexander

    2009-02-01

    A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).

  9. A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times

    PubMed Central

    Ferraioli, Giampaolo; Pascazio, Vito

    2015-01-01

    Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets. PMID:26798631

  10. Anterior segment epibulbar choristoma containing brain tissue and with aphakia.

    PubMed

    Ullah, Muhammad Aman; Venkatraman, Bhat; Mujeeb, Imaad

    2007-01-01

    We report an unusual case of an epibulbar choristoma in a neonate born with a mass arising from the cornea. Radiologic examination showed focal corneal bulge with absence of the lens. Histologic study revealed the lesion was an epibulbar choristoma composed of only brain tissue.

  11. Histogram analysis of ADC in brain tumor patients

    NASA Astrophysics Data System (ADS)

    Banerjee, Debrup; Wang, Jihong; Li, Jiang

    2011-03-01

    At various stage of progression, most brain tumors are not homogenous. In this presentation, we retrospectively studied the distribution of ADC values inside tumor volume during the course of tumor treatment and progression for a selective group of patients who underwent an anti-VEGF trial. Complete MRI studies were obtained for this selected group of patients including pre- and multiple follow-up, post-treatment imaging studies. In each MRI imaging study, multiple scan series were obtained as a standard protocol which includes T1, T2, T1-post contrast, FLAIR and DTI derived images (ADC, FA etc.) for each visit. All scan series (T1, T2, FLAIR, post-contrast T1) were registered to the corresponding DTI scan at patient's first visit. Conventionally, hyper-intensity regions on T1-post contrast images are believed to represent the core tumor region while regions highlighted by FLAIR may overestimate tumor size. Thus we annotated tumor regions on the T1-post contrast scans and ADC intensity values for pixels were extracted inside tumor regions as defined on T1-post scans. We fit a mixture Gaussian (MG) model for the extracted pixels using the Expectation-Maximization (EM) algorithm, which produced a set of parameters (mean, various and mixture coefficients) for the MG model. This procedure was performed for each visits resulting in a series of GM parameters. We studied the parameters fitted for ADC and see if they can be used as indicators for tumor progression. Additionally, we studied the ADC characteristics in the peri-tumoral region as identified by hyper-intensity on FLAIR scans. The results show that ADC histogram analysis of the tumor region supports the two compartment model that suggests the low ADC value subregion corresponding to densely packed cancer cell while the higher ADC value region corresponding to a mixture of viable and necrotic cells with superimposed edema. Careful studies of the composition and relative volume of the two compartments in tumor

  12. Intraparenchymal hemorrhage segmentation from clinical head CT of patients with traumatic brain injury

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; Wilkes, Sean; Diaz-Arrastia, Ramon; Butman, John A.; Pham, Dzung L.

    2015-03-01

    Quantification of hemorrhages in head computed tomography (CT) images from patients with traumatic brain injury (TBI) has potential applications in monitoring disease progression and better understanding of the patho-physiology of TBI. Although manual segmentations can provide accurate measures of hemorrhages, the processing time and inter-rater variability make it infeasible for large studies. In this paper, we propose a fully automatic novel pipeline for segmenting intraparenchymal hemorrhages (IPH) from clinical head CT images. Unlike previous methods of model based segmentation or active contour techniques, we rely on relevant and matching examples from already segmented images by trained raters. The CT images are first skull-stripped. Then example patches from an "atlas" CT and its manual segmentation are used to learn a two-class sparse dictionary for hemorrhage and normal tissue. Next, for a given "subject" CT, a subject patch is modeled as a sparse convex combination of a few atlas patches from the dictionary. The same convex combination is applied to the atlas segmentation patches to generate a membership for the hemorrhages at each voxel. Hemorrhages are segmented from 25 subjects with various degrees of TBI. Results are compared with segmentations obtained from an expert rater. A median Dice coefficient of 0.85 between automated and manual segmentations is achieved. A linear fit between automated and manual volumes show a slope of 1.0047, indicating a negligible bias in volume estimation.

  13. A patient-specific segmentation framework for longitudinal MR images of traumatic brain injury

    NASA Astrophysics Data System (ADS)

    Wang, Bo; Prastawa, Marcel; Irimia, Andrei; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.; Gerig, Guido

    2012-02-01

    Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Robust, reproducible segmentations of MR images with TBI are crucial for quantitative analysis of recovery and treatment efficacy. However, this is a significant challenge due to severe anatomy changes caused by edema (swelling), bleeding, tissue deformation, skull fracture, and other effects related to head injury. In this paper, we introduce a multi-modal image segmentation framework for longitudinal TBI images. The framework is initialized through manual input of primary lesion sites at each time point, which are then refined by a joint approach composed of Bayesian segmentation and construction of a personalized atlas. The personalized atlas construction estimates the average of the posteriors of the Bayesian segmentation at each time point and warps the average back to each time point to provide the updated priors for Bayesian segmentation. The difference between our approach and segmenting longitudinal images independently is that we use the information from all time points to improve the segmentations. Given a manual initialization, our framework automatically segments healthy structures (white matter, grey matter, cerebrospinal fluid) as well as different lesions such as hemorrhagic lesions and edema. Our framework can handle different sets of modalities at each time point, which provides flexibility in analyzing clinical scans. We show results on three subjects with acute baseline scans and chronic follow-up scans. The results demonstrate that joint analysis of all the points yields improved segmentation compared to independent analysis of the two time points.

  14. Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method.

    PubMed

    Archibald, Rick; Chen, Kewei; Gelb, Anne; Renaut, Rosemary

    2003-09-01

    The Gegenbauer image reconstruction method, previously shown to improve the quality of magnetic resonance images, is utilized in this study as a segmentation preprocessing step. It is demonstrated that, for all simulated and real magnetic resonance images used in this study, the Gegenbauer reconstruction method improves the accuracy of segmentation. Although it is more desirable to use the k-space data for the Gegenbauer reconstruction method, only information acquired from MR images is necessary for the reconstruction, making the procedure completely self-contained and viable for all human brain segmentation algorithms. PMID:14527609

  15. Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

    NASA Astrophysics Data System (ADS)

    Hajiesmaeili, Maryam; Dehmeshki, Jamshid; Bagheri Nakhjavanlo, Bashir; Ellis, Tim

    2014-04-01

    Shrinkage of the hippocampus is a primary biomarker for Alzheimer's disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

  16. Electroretinography and Visual Evoked Potentials in Childhood Brain Tumor Survivors.

    PubMed

    Pietilä, Sari; Lenko, Hanna L; Oja, Sakari; Koivisto, Anna-Maija; Pietilä, Timo; Mäkipernaa, Anne

    2016-07-01

    This population-based cross-sectional study evaluates the clinical value of electroretinography and visual evoked potentials in childhood brain tumor survivors. A flash electroretinography and a checkerboard reversal pattern visual evoked potential (or alternatively a flash visual evoked potential) were done for 51 survivors (age 3.8-28.7 years) after a mean follow-up time of 7.6 (1.5-15.1) years. Abnormal electroretinography was obtained in 1 case, bilaterally delayed abnormal visual evoked potentials in 22/51 (43%) cases. Nine of 25 patients with infratentorial tumor location, and altogether 12 out of 31 (39%) patients who did not have tumors involving the visual pathways, had abnormal visual evoked potentials. Abnormal electroretinographies are rarely observed, but abnormal visual evoked potentials are common even without evident anatomic lesions in the visual pathway. Bilateral changes suggest a general and possibly multifactorial toxic/adverse effect on the visual pathway. Electroretinography and visual evoked potential may have clinical and scientific value while evaluating long-term effects of childhood brain tumors and tumor treatment.

  17. Pediatric Brain Tumor Treatment: Growth Consequences and their Management

    PubMed Central

    Mostoufi-Moab, Sogol; Grimberg, Adda

    2014-01-01

    Tumors of the central nervous system, the most common solid tumors of childhood, are a major source of cancer-related morbidity and mortality in children. Survival rates have improved significantly following treatment for childhood brain tumors, with this growing cohort of survivors at high risk of adverse medical and late effects. Endocrine morbidities are the most prominent disorder among the spectrum of long-term conditions, with growth hormone deficiency the most common endocrinopathy noted, either from tumor location or after cranial irradiation and treatment effects on the hypothalamic/pituitary unit. Deficiency of other anterior pituitary hormones can contribute to negative effects on growth, body image and composition, sexual function, skeletal health, and quality of life. Pediatric and adult endocrinologists often provide medical care to this increasing population. Therefore, a thorough understanding of the epidemiology and pathophysiology of growth failure as a consequence of childhood brain tumor, both during and after treatment, is necessary and the main focus of this review. PMID:21037539

  18. Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes.

    PubMed

    Subbanna, Nagesh K; Precup, Doina; Collins, D Louis; Arbel, Tal

    2013-01-01

    In this paper, we present a fully automated hierarchical probabilistic framework for segmenting brain tumours from multispectral human brain magnetic resonance images (MRIs) using multiwindow Gabor filters and an adapted Markov Random Field (MRF) framework. In the first stage, a customised Gabor decomposition is developed, based on the combined-space characteristics of the two classes (tumour and non-tumour) in multispectral brain MRIs in order to optimally separate tumour (including edema) from healthy brain tissues. A Bayesian framework then provides a coarse probabilistic texture-based segmentation of tumours (including edema) whose boundaries are then refined at the voxel level through a modified MRF framework that carefully separates the edema from the main tumour. This customised MRF is not only built on the voxel intensities and class labels as in traditional MRFs, but also models the intensity differences between neighbouring voxels in the likelihood model, along with employing a prior based on local tissue class transition probabilities. The second inference stage is shown to resolve local inhomogeneities and impose a smoothing constraint, while also maintaining the appropriate boundaries as supported by the local intensity difference observations. The method was trained and tested on the publicly available MICCAI 2012 Brain Tumour Segmentation Challenge (BRATS) Database [1] on both synthetic and clinical volumes (low grade and high grade tumours). Our method performs well compared to state-of-the-art techniques, outperforming the results of the top methods in cases of clinical high grade and low grade tumour core segmentation by 40% and 45% respectively.

  19. Dysembryoplastic neuroepithelial tumor: A rare brain tumor not to be misdiagnosed

    PubMed Central

    Sukheeja, Deepti; Mehta, Jayanti

    2016-01-01

    Dysembryoplastic neuroepithelial tumor (DNET) is a recently described, morphologically unique, and surgically curable low-grade brain tumor which is included in the latest WHO classification as neuronal and mixed neuronal-glial tumor. It is usually seen in children and young adults. The importance of this particular entity is that it is a surgically curable neuroepithelial neoplasm. When recognized, the need for adjuvant radiotherapy and chemotherapy is obviated. We hereby present a case report of an 8-year-old male child who presented with intractable seizures and parieto-occipital space occupying lesion. Histologically, the tumor exhibited features of WHO grade I dysembryoplastic neuroepithelial tumor which was further confirmed by immunohistochemistry. PMID:27057233

  20. Numerical simulations of MREIT conductivity imaging for brain tumor detection.

    PubMed

    Meng, Zi Jun; Sajib, Saurav Z K; Chauhan, Munish; Sadleir, Rosalind J; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

    2013-01-01

    Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged. PMID:23737862

  1. Numerical Simulations of MREIT Conductivity Imaging for Brain Tumor Detection

    PubMed Central

    Meng, Zi Jun; Sajib, Saurav Z. K.; Chauhan, Munish; Sadleir, Rosalind J.; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

    2013-01-01

    Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged. PMID:23737862

  2. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

    PubMed Central

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-01-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

  3. 3D+t brain MRI segmentation using robust 4D Hidden Markov Chain.

    PubMed

    Lavigne, François; Collet, Christophe; Armspach, Jean-Paul

    2014-01-01

    In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account the temporal correlation in addition to spatial neighbourhood information. To improve the robustness of the segmentation of the principal brain structures and to detect Multiple Sclerosis Lesions as outliers the Trimmed Likelihood Estimator (TLE) is used during the process. The method is validated on 3D+t brain MR images. PMID:25571045

  4. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling.

    PubMed

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Sastre-Garriga, Jaume; Montalban, Xavier; Rovira, Àlex; Lladó, Xavier

    2015-01-01

    Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.

  5. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

    PubMed Central

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; Pareto, Deborah; Vilanova, Joan C.; Ramió-Torrentà, Lluís; Sastre-Garriga, Jaume; Montalban, Xavier; Rovira, Àlex; Lladó, Xavier

    2015-01-01

    Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations. PMID:26740917

  6. Collecting and Storing Blood and Brain Tumor Tissue Samples From Children With Brain Tumors

    ClinicalTrials.gov

    2016-05-17

    Childhood Atypical Teratoid/Rhabdoid Tumor; Childhood Central Nervous System Germ Cell Tumor; Childhood Choroid Plexus Tumor; Childhood Craniopharyngioma; Childhood Grade I Meningioma; Childhood Grade II Meningioma; Childhood Grade III Meningioma; Childhood High-grade Cerebral Astrocytoma; Childhood Infratentorial Ependymoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Oligodendroglioma; Childhood Supratentorial Ependymoma; Newly Diagnosed Childhood Ependymoma; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Childhood Visual Pathway and Hypothalamic Glioma; Recurrent Childhood Visual Pathway Glioma

  7. Use of chlorotoxin for targeting of primary brain tumors.

    PubMed

    Soroceanu, L; Gillespie, Y; Khazaeli, M B; Sontheimer, H

    1998-11-01

    Gliomas are primary brain tumors that arise from differentiated glial cells through a poorly understood malignant transformation. Although glioma cells retain some genetic and antigenic features common to glial cells, they show a remarkable degree of antigenic heterogeneity and variable mutations in their genome. Glioma cells have recently been shown to express a glioma-specific chloride ion channel (GCC) that is sensitive to chlorotoxin (CTX), a small peptide purified from Leiurus quinquestriatus scorpion venom [N. Ullrich et al, Neuroreport, 7: 1020-1024, 1996; and N. Ullrich and H. Sontheimer, Am. J. Physiol. (Cell Physiol.), 270: C1511-C1521, 1996]. Using native and recombinant 125I-labeled CTX, we show that toxin binding to glioma cells is specific and involves high affinity [dissociation constant (Kd)=4.2 nM] and low affinity (Kd=660 nml) binding sites. In radioreceptor assays, 125I-labeled CTX binds to a protein with Mr=72,000, presumably GCC or a receptor that modulates GCC activity. In vivo targeting and biodistribution experiments were obtained using 125I- and (131)I-labeled CTX injected into severe combined immunodeficient mice bearing xenografted gliomas. CTX selectively accumulated in the brain of tumor-bearing mice with calculated brain: muscle ratios of 36.4% of injected dose/g (ID/g), as compared to 12.4% ID/g in control animals. In the tumor-bearing severe combined immunodeficient mice, the vast majority of the brain-associated radioactivity was localized within the tumor (tumor:muscle ratio, 39.13% ID/g; contralateral brain:muscle ratio, 6.68%ID/g). Moreover, (131)I-labeled CTX distribution, visualized through in vivo imaging by gamma ray camera scans, demonstrates specific and persistent intratumoral localization of the radioactive ligand. Immunohistochemical studies using biotinylated and fluorescently tagged CTX show highly selective staining of glioma cells in vitro, in situ, and in sections of patient biopsies. Comparison tissues including

  8. Drosophila neural stem cells in brain development and tumor formation.

    PubMed

    Jiang, Yanrui; Reichert, Heinrich

    2014-01-01

    Neuroblasts, the neural stem cells in Drosophila, generate the complex neural structure of the central nervous system. Significant progress has been made in understanding the mechanisms regulating the self-renewal, proliferation, and differentiation in Drosophila neuroblast lineages. Deregulation of these mechanisms can lead to severe developmental defects and the formation of malignant brain tumors. Here, the authors review the molecular genetics of Drosophila neuroblasts and discuss some recent advances in stem cell and cancer biology using this model system.

  9. Liposomally formulated phospholipid-conjugated indocyanine green for intra-operative brain tumor detection and resection.

    PubMed

    Suganami, Akiko; Iwadate, Yasuo; Shibata, Sayaka; Yamashita, Masamichi; Tanaka, Tsutomu; Shinozaki, Natsuki; Aoki, Ichio; Saeki, Naokatsu; Shirasawa, Hiroshi; Okamoto, Yoshiharu; Tamura, Yutaka

    2015-12-30

    Some tumor-specific near-infrared (NIR) fluorescent dyes such as indocyanine green (ICG), IDRye800CW, and 5-aminolevulinic acid have been used clinically for detecting tumor margins or micro-cancer lesions. In this study, we evaluated the physicochemical properties of liposomally formulated phospholipid-conjugated ICG, denoted by LP-iDOPE, as a clinically translatable NIR imaging nanoparticle for brain tumors. We also confirmed its brain-tumor-specific biodistribution and its characteristics as the intra-operative NIR imaging nanoparticles for brain tumor surgery. These properties of LP-iDOPE may enable neurosurgeons to achieve more accurate identification and more complete resection of brain tumor.

  10. Vasculature segmentation for radio frequency ablation of non-resectable hepatic tumors

    NASA Astrophysics Data System (ADS)

    Hemler, Paul F.; McCreedy, Evan S.; Cheng, Ruida; Wood, Brad; McAuliffe, Matthew J.

    2006-03-01

    In Radio Frequency Ablation (RFA) procedures, hepatic tumor tissue is heated to a temperature where necrosis is insured. Unfortunately, recent results suggest that heating tumor tissue to necrosis is complicated because nearby major blood vessels provide a cooling effect. Therefore, it is fundamentally important for physicians to perform a careful analysis of the spatial relationship of diseased tissue to larger liver blood vessels. The liver contains many of these large vessels, which affect the RFA ablation shape and size. There are many sophisticated vasculature detection and segmentation techniques reported in the literature that identify continuous vessels as the diameter changes size and it transgresses through many bifurcation levels. However, the larger blood vessels near the treatment area are the only vessels required for proper RFA treatment plan formulation and analysis. With physician guidance and interaction, our system can segment those vessels which are most likely to affect the RFA ablations. We have found that our system provides the physician with therapeutic, geometric and spatial information necessary to accurately plan treatment of tumors near large blood vessels. The segmented liver vessels near the treatment region are also necessary for computing isolevel heating profiles used to evaluate different proposed treatment configurations.

  11. Heavy Metals and Epigenetic Alterations in Brain Tumors

    PubMed Central

    Caffo, Maria; Caruso, Gerardo; Fata, Giuseppe La; Barresi, Valeria; Visalli, Maria; Venza, Mario; Venza, Isabella

    2014-01-01

    Heavy metals and their derivatives can cause various diseases. Numerous studies have evaluated the possible link between exposure to heavy metals and various cancers. Recent data show a correlation between heavy metals and aberration of genetic and epigenetic patterns. From a literature search we noticed few experimental and epidemiological studies that evaluate a possible correlation between heavy metals and brain tumors. Gliomas arise due to genetic and epigenetic alterations of glial cells. Changes in gene expression result in the alteration of the cellular division process. Epigenetic alterations in brain tumors include the hypermethylation of CpG group, hypomethylation of specific genes, aberrant activation of genes, and changes in the position of various histones. Heavy metals are capable of generating reactive oxygen assumes that key functions in various pathological mechanisms. Alteration of homeostasis of metals could cause the overproduction of reactive oxygen species and induce DNA damage, lipid peroxidation, and alteration of proteins. In this study we summarize the possible correlation between heavy metals, epigenetic alterations and brain tumors. We report, moreover, the review of relevant literature. PMID:25646073

  12. Sigma and opioid receptors in human brain tumors

    SciTech Connect

    Thomas, G.E.; Szuecs, M.; Mamone, J.Y.; Bem, W.T.; Rush, M.D.; Johnson, F.E.; Coscia, C.J. )

    1990-01-01

    Human brain tumors and nude mouse-borne human neuroblastomas and gliomas were analyzed for sigma and opioid receptor content. Sigma binding was assessed using ({sup 3}H) 1, 3-di-o-tolylguanidine (DTG), whereas opioid receptor subtypes were measured with tritiated forms of the following: {mu}, (D-ala{sup 2}, mePhe{sup 4}, gly-ol{sup 5}) enkephalin (DAMGE); {kappa}, ethylketocyclazocine (EKC) or U69,593; {delta}, (D-pen{sup 2}, D-pen{sup 5}) enkephalin (DPDPE) or (D-ala{sup 2}, D-leu{sup 5}) enkephalin (DADLE) with {mu} suppressor present. Binding parameters were estimated by homologous displacement assays followed by analysis using the LIGAND program. Sigma binding was detected in 15 of 16 tumors examined with very high levels found in a brain metastasis from an adenocarcinoma of lung and a human neuroblastoma (SK-N-MC) passaged in nude mice. {kappa} opioid receptor binding was detected in 4 of 4 glioblastoma multiforme specimens and 2 of 2 human astrocytoma cell lines tested but not in the other brain tumors analyzed.

  13. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation.

    PubMed

    Roy, Snehashis; Carass, Aaron; Pacheco, Jennifer; Bilgel, Murat; Resnick, Susan M; Prince, Jerry L; Pham, Dzung L

    2016-01-01

    Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4-12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis. PMID:26958465

  14. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation

    PubMed Central

    Roy, Snehashis; Carass, Aaron; Pacheco, Jennifer; Bilgel, Murat; Resnick, Susan M.; Prince, Jerry L.; Pham, Dzung L.

    2016-01-01

    Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4–12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis. PMID:26958465

  15. A skull segmentation method for brain MR images based on multiscale bilateral filtering scheme

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2010-03-01

    We present a novel automatic segmentation method for the skull on brain MR images for attenuation correction in combined PET/MRI applications. Our method transforms T1-weighted MR images to the Radon domain and then detects the feature of the skull. In the Radon domain we use a bilateral filter to construct a multiscale images series. For the repeated convolution we increase the spatial smoothing at each scale and make the cumulative width of the spatial and range Gaussian doubled at each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The method is robust for noise MR images because of its multiscale bilateral filtering scheme. After combining the two filtered sinogram, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. We use the filtered back projection method to reconstruct the segmented skull image. We define six metrics to evaluate our segmentation method. The method has been tested with brain phantom data, simulated brain data, and real MRI data. Evaluation results showed that our method is robust and accurate, which is useful for skull segmentation and subsequently for attenuation correction in combined PET/MRI applications.

  16. Follow-up segmentation of lung tumors in PET and CT data

    NASA Astrophysics Data System (ADS)

    Opfer, Roland; Kabus, Sven; Schneider, Torben; Carlsen, Ingwer C.; Renisch, Steffen; Sabczynski, Jörg

    2009-02-01

    Early response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. We have developed algorithms which allow the user to track both tumor volume and standardized uptake value (SUV) measurements during the therapy from series of CT and PET images, respectively. To prepare for tumor volume estimation we have developed a new technique for a fast, flexible, and intuitive 3D definition of meshes. This initial surface is then automatically adapted by means of a model-based segmentation algorithm and propagated to each follow-up scan. If necessary, manual corrections can be added by the user. To determine SUV measurements a prioritized region growing algorithm is employed. For an improved workflow all algorithms are embedded in a PET/CT therapy monitoring software suite giving the clinician a unified and immediate access to all data sets. Whenever the user clicks on a tumor in a base-line scan, the courses of segmented tumor volumes and SUV measurements are automatically identified and displayed to the user as a graph plot. According to each course, the therapy progress can be classified as complete or partial response or as progressive or stable disease. We have tested our methods with series of PET/CT data from 9 lung cancer patients acquired at Princess Margaret Hospital in Toronto. Each patient underwent three PET/CT scans during a radiation therapy. Our results indicate that a combination of mean metabolic activity in the tumor with the PET-based tumor volume can lead to an earlier response detection than a purely volume based (CT diameter) or purely functional based (e.g. SUV max or SUV mean) response measures. The new software seems applicable for easy, faster, and reproducible quantification to routinely monitor tumor therapy.

  17. The p53 gene and protein in human brain tumors

    SciTech Connect

    Louis, D.N. )

    1994-01-01

    Because p53 gene alterations are commonplace in human tumors and because p53 protein is involved in a number of important cellular pathways, p53 has become a topic of intensive investigation, both by basic scientists and clinicians. p53 was initially identified by two independent laboratories in 1979 as a 53 kilodalton (kD) protein that complexes with the large T antigen of SV40 virus. Shortly thereafter, it was shown that the E1B oncoprotein of adenovirus also binds p53. The binding of two different oncogenic viral tumor proteins to the same cellular protein suggested that p53 might be integral to tumorigenesis. The human p53 cDNA and gene were subsequently cloned in the mid-1980s, and analysis of p53 gene alterations in human tumors followed a few year later. During these 10 years, researchers grappling with the vagaries of p53 first characterized the gene as an oncogene, then as a tumor suppressor gene, and most recently as both a tumor suppressor gene and a so-called [open quotes]dominant negative[close quotes] oncogene. The last few years have seen an explosion in work on this single gene and its protein product. A review of a computerized medical database revealed approximately 650 articles on p53 in 1992 alone. p53 has assumed importance in neuro-oncology because p53 mutations and protein alterations are frequent in the common diffuse, fibrillary astrocytic tumors of adults. p53 mutations in astrocytomas were first described in 1989 and were followed by more extensive analyses of gene mutations and protein alterations in adult astrocytomas. The gene has also been studied in less common brain tumors. Elucidating the role of p53 in brain tumorigenesis will not only enhance understanding of brain tumor biology but may also contribute to improved diagnosis and therapy. This discussion reviews key aspects of the p53 gene and protein, and describe their emerging roles in central nervous system neoplasia. 102 refs., 6 figs., 1 tab.

  18. Prospective study of neuropsychological sequelae in children with brain tumors

    SciTech Connect

    Bordeaux, J.D.; Dowell, R.E. Jr.; Copeland, D.R.; Fletcher, J.M.; Francis, D.J.; van Eys, J.

    1988-01-01

    Surgery and radiotherapy are the primary modalities of treatment for pediatric brain tumors. Despite the widespread use of these treatments, little is known of their acute effects (within one year posttreatment) on neuropsychological functions. An understanding of acute treatment effects may provide valuable feedback to neurosurgeons and a baseline against which delayed sequelae may be evaluated. This study compares pre- and posttherapy neuropsychological test performance of pediatric brain tumor patients categorized into two groups on the basis of treatment modalities: surgery (n = 7) and radiotherapy (n = 7). Treatment groups were composed of children aged 56 to 196 months at the time of evaluation with heterogeneous tumor diagnoses and locations. Comparisons of pretherapy findings with normative values using confidence intervals indicated that both groups performed within the average range on most measures. Outstanding deficits at baseline were observed on tests of fine-motor, psychomotor, and timed language skills, and are likely to be attributable to tumor-related effects. Comparisons of pre- versus posttherapy neuropsychological test findings indicated no significant interval changes for either group. Results suggest that surgery and radiotherapy are not associated with acute effects on neuropsychological functions.

  19. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans.

    PubMed

    Mendrik, Adriënne M; Vincken, Koen L; Kuijf, Hugo J; Breeuwer, Marcel; Bouvy, Willem H; de Bresser, Jeroen; Alansary, Amir; de Bruijne, Marleen; Carass, Aaron; El-Baz, Ayman; Jog, Amod; Katyal, Ranveer; Khan, Ali R; van der Lijn, Fedde; Mahmood, Qaiser; Mukherjee, Ryan; van Opbroek, Annegreet; Paneri, Sahil; Pereira, Sérgio; Persson, Mikael; Rajchl, Martin; Sarikaya, Duygu; Smedby, Örjan; Silva, Carlos A; Vrooman, Henri A; Vyas, Saurabh; Wang, Chunliang; Zhao, Liang; Biessels, Geert Jan; Viergever, Max A

    2015-01-01

    Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand. PMID:26759553

  20. Robust kernelized local information fuzzy C-means clustering for brain magnetic resonance image segmentation.

    PubMed

    Elazab, Ahmed; AbdulAzeem, Yousry M; Wu, Shiqian; Hu, Qingmao

    2016-03-17

    Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity. PMID:27257884

  1. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

    PubMed Central

    Mendrik, Adriënne M.; Vincken, Koen L.; Kuijf, Hugo J.; Breeuwer, Marcel; Bouvy, Willem H.; de Bresser, Jeroen; Alansary, Amir; de Bruijne, Marleen; Carass, Aaron; El-Baz, Ayman; Jog, Amod; Katyal, Ranveer; Khan, Ali R.; van der Lijn, Fedde; Mahmood, Qaiser; Mukherjee, Ryan; van Opbroek, Annegreet; Paneri, Sahil; Pereira, Sérgio; Rajchl, Martin; Sarikaya, Duygu; Smedby, Örjan; Silva, Carlos A.; Vrooman, Henri A.; Vyas, Saurabh; Wang, Chunliang; Zhao, Liang; Biessels, Geert Jan; Viergever, Max A.

    2015-01-01

    Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand. PMID:26759553

  2. Automated segmentation of tumors on bone scans using anatomy-specific thresholding

    NASA Astrophysics Data System (ADS)

    Chu, Gregory H.; Lo, Pechin; Kim, Hyun J.; Lu, Peiyun; Ramakrishna, Bharath; Gjertson, David; Poon, Cheryce; Auerbach, Martin; Goldin, Jonathan; Brown, Matthew S.

    2012-03-01

    Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069.

  3. Atlas-based segmentation of deep brain structures using non-rigid registration

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Faisal; Mewes, Klaus; Gross, Robert E.; Škrinjar, Oskar

    2008-03-01

    Deep brain structures are frequently used as targets in neurosurgical procedures. However, the boundaries of these structures are often not visible in clinically used MR and CT images. Techniques based on anatomical atlases and indirect targeting are used to infer the location of these targets intraoperatively. Initial errors of such approaches may be up to a few millimeters, which is not negligible. E.g. subthalamic nucleus is approximately 4x6 mm in the axial plane and the diameter of globus pallidus internus is approximately 8 mm, both of which are used as targets in deep brain stimulation surgery. To increase the initial localization accuracy of deep brain structures we have developed an atlas-based segmentation method that can be used for the surgery planning. The atlas is a high resolution MR head scan of a healthy volunteer with nine deep brain structures manually segmented. The quality of the atlas image allowed for the segmentation of the deep brain structures, which is not possible from the clinical MR head scans of patients. The subject image is non-rigidly registered to the atlas image using thin plate splines to represent the transformation and normalized mutual information as a similarity measure. The obtained transformation is used to map the segmented structures from the atlas to the subject image. We tested the approach on five subjects. The quality of the atlas-based segmentation was evaluated by visual inspection of the third and lateral ventricles, putamena, and caudate nuclei, which are visible in the subject MR images. The agreement of these structures for the five tested subjects was approximately 1 to 2 mm.

  4. Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

    PubMed

    Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

    2016-01-01

    Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

  5. Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

    PubMed Central

    Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

    2016-01-01

    Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

  6. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI.

    PubMed

    Sauwen, Nicolas; Sima, Diana M; Van Cauter, Sofie; Veraart, Jelle; Leemans, Alexander; Maes, Frederik; Himmelreich, Uwe; Van Huffel, Sabine

    2015-12-01

    Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was

  7. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI.

    PubMed

    Sauwen, Nicolas; Sima, Diana M; Van Cauter, Sofie; Veraart, Jelle; Leemans, Alexander; Maes, Frederik; Himmelreich, Uwe; Van Huffel, Sabine

    2015-12-01

    Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was

  8. Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps

    PubMed Central

    Helms, Gunther; Draganski, Bogdan; Frackowiak, Richard; Ashburner, John; Weiskopf, Nikolaus

    2009-01-01

    Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures. PMID:19344771

  9. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    PubMed Central

    A., Javadpour; A., Mohammadi

    2016-01-01

    Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

  10. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    PubMed Central

    A., Javadpour; A., Mohammadi

    2016-01-01

    Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases.

  11. Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI.

    PubMed

    Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

    2016-01-01

    This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images. PMID:26904103

  12. Radiosurgery in the management of pediatric brain tumors.

    PubMed

    Raco, A; Raimondi, A J; D'Alonzo, A; Esposito, V; Valentino, V

    2000-05-01

    A total of 114 patients with benign and malignant intracranial tumors were treated by Valentino at the Flaminia Radiosurgical Center using a Philips 6-MeV linear accelerator between 1987 and 1995. The tumor locations break down as follows: 36 in the cerebral hemispheres, 14 in the region of the hypothalamus/optic chiasm, 21 in the III ventricle/pineal region, 3 in the basal ganglia, 27 in the posterior fossa, 13 in the brain stem. Seventy-nine patients had multivariate/combined treatment consisting of surgery or biopsy followed by chemotherapy, radiotherapy and/or radiosurgery. Thirty-five were not operated on or biopsied but were treated primarily by radiosurgery, which was associated with chemotherapy and conventional radiotherapy. The short- and long-term results were evaluated separately for each pathology in an attempt to derive guidelines for future treatment. For tumors of the pineal region, we are of the opinion that radiosurgery is the treatment of choice in children and that more than one-third of patients can be cured by this means. The remaining patients require surgery and/or chemotherapy in addition. For medulloblastomas radiosurgery may be useful to control local recurrence if coupled with chemotherapy. In the case of ependymomas, partly because of the extreme malignancy of the lesions in our series, radiosurgery did not succeed in controlling local recurrence. We fear that limiting treatment to radiosurgery, rather than prescribing conventional radiotherapy when indicated, could permit CNS seeding. For craniopharyngiomas radiosurgery proved useful for controlling solid remnants. In glial tumors radiosurgery helped either to "sterilize" the tumor bed after removal or to treat remnants of the lesions in critical areas; for diffuse brain stem gliomas it should be considered the treatment of choice.

  13. Image updating for brain deformation compensation in tumor resection

    NASA Astrophysics Data System (ADS)

    Fan, Xiaoyao; Ji, Songbai; Olson, Jonathan D.; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

    2016-03-01

    Preoperative magnetic resonance images (pMR) are typically used for intraoperative guidance in image-guided neurosurgery, the accuracy of which can be significantly compromised by brain deformation. Biomechanical finite element models (FEM) have been developed to estimate whole-brain deformation and produce model-updated MR (uMR) that compensates for brain deformation at different surgical stages. Early stages of surgery, such as after craniotomy and after dural opening, have been well studied, whereas later stages after tumor resection begins remain challenging. In this paper, we present a method to simulate tumor resection by incorporating data from intraoperative stereovision (iSV). The amount of tissue resection was estimated from iSV using a "trial-and-error" approach, and the cortical shift was measured from iSV through a surface registration method using projected images and an optical flow (OF) motion tracking algorithm. The measured displacements were employed to drive the biomechanical brain deformation model, and the estimated whole-brain deformation was subsequently used to deform pMR and produce uMR. We illustrate the method using one patient example. The results show that the uMR aligned well with iSV and the overall misfit between model estimates and measured displacements was 1.46 mm. The overall computational time was ~5 min, including iSV image acquisition after resection, surface registration, modeling, and image warping, with minimal interruption to the surgical flow. Furthermore, we compare uMR against intraoperative MR (iMR) that was acquired following iSV acquisition.

  14. Magnetic nanoparticles: an emerging technology for malignant brain tumor imaging and therapy

    PubMed Central

    Wankhede, Mamta; Bouras, Alexandros; Kaluzova, Milota; Hadjipanayis, Costas G

    2012-01-01

    Magnetic nanoparticles (MNPs) represent a promising nanomaterial for the targeted therapy and imaging of malignant brain tumors. Conjugation of peptides or antibodies to the surface of MNPs allows direct targeting of the tumor cell surface and potential disruption of active signaling pathways present in tumor cells. Delivery of nanoparticles to malignant brain tumors represents a formidable challenge due to the presence of the blood–brain barrier and infiltrating cancer cells in the normal brain. Newer strategies permit better delivery of MNPs systemically and by direct convection-enhanced delivery to the brain. Completion of a human clinical trial involving direct injection of MNPs into recurrent malignant brain tumors for thermotherapy has established their feasibility, safety and efficacy in patients. Future translational studies are in progress to understand the promising impact of MNPs in the treatment of malignant brain tumors. PMID:22390560

  15. Topology polymorphism graph for lung tumor segmentation in PET-CT images

    NASA Astrophysics Data System (ADS)

    Cui, Hui; Wang, Xiuying; Zhou, Jianlong; Eberl, Stefan; Yin, Yong; Feng, Dagan; Fulham, Michael

    2015-06-01

    Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a ‘topo-poly’ graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an ‘isolated’ group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a ‘complex’ group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice’s similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881  ±  0.046 and HD of 5.311  ±  3.022 mm for the isolated cases and DSC of 0.870  ±  0.038 and HD of 9.370  ±  3.169 mm for the complex cases. Student’s t-test showed that our model outperformed the other methods (p-values <0.05).

  16. Supervised segmentation of MRI brain images using combination of multiple classifiers.

    PubMed

    Ahmadvand, Ali; Sharififar, Mohammad; Daliri, Mohammad Reza

    2015-06-01

    Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of combination of multiple classifiers (CMCs) methods is proposed. The proposed method uses dynamic local training sets instead of a full statics one and also it change the classifier rank criterion properly for brain tissue classification. Selection policy for combining the different decisions is implemented here and the K-nearest neighbor algorithm is used to find the best local classifier. Experimental results show that the proposed method can classify the real datasets of the internet brain segmentation repository better than all single classifiers in ensemble and produces significantly improvement on other CMCs methods. PMID:26130310

  17. Hybrid atlas-based and image-based approach for segmenting 3D brain MRIs

    NASA Astrophysics Data System (ADS)

    Bueno, Gloria; Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

    2001-07-01

    This work is a contribution to the problem of localizing key cerebral structures in 3D MRIs and its quantitative evaluation. In pursuing it, the cooperation between an image-based segmentation method and a hierarchical deformable registration approach has been considered. The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D regions of an image, I(s), from the data set are identified. These regions, M(I), are obtained combining information from deformable atlas, achieved by the warping of eight previous labeled maps on I(s). Then, the goal of the decision stage is to precisely locate the contours of the 3D regions set by the markers. This contour decision is performed by a 3D extension of the watershed transform. The anatomical structures taken into consideration and embedded into the atlas are brain, ventricles, corpus callosum, cerebellum, right and left hippocampus, medulla and midbrain. The hybrid method operates fully automatically and in 3D, successfully providing segmented brain structures. The quality of the segmentation has been studied in terms of the detected volume ratio by using kappa statistic and ROC analysis. Results of the method are shown and validated on a 3D MRI phantom. This study forms part of an on-going long term research aiming at the creation of a 3D probabilistic multi-purpose anatomical brain atlas.

  18. Automated segmentation of brain ventricles in unenhanced CT of patients with ischemic stroke

    NASA Astrophysics Data System (ADS)

    Qian, Xiaohua; Wang, Jiahui; Li, Qiang

    2013-02-01

    We are developing an automated method for detection and quantification of ischemic stroke in computed tomography (CT). Ischemic stroke often connects to brain ventricle, therefore, ventricular segmentation is an important and difficult task when stroke is present, and is the topic of this study. We first corrected inclination angle of brain by aligning midline of brain with the vertical centerline of a slice. We then estimated the intensity range of the ventricles by use of the k-means method. Two segmentation of the ventricle were obtained by use of thresholding technique. One segmentation contains ventricle and nearby stroke. The other mainly contains ventricle. Therefore, the stroke regions can be extracted and removed using image difference technique. An adaptive template-matching algorithm was employed to identify objects in the fore-mentioned segmentation. The largest connected component was identified and considered as the ventricle. We applied our method to 25 unenhanced CT scans with stroke. Our method achieved average Dice index, sensitivity, and specificity of 95.1%, 97.0%, and 99.8% for the entire ventricular regions. The experimental results demonstrated that the proposed method has great potential in detection and quantification of stroke and other neurologic diseases.

  19. Boron Neutron Capture Therapy for Malignant Brain Tumors

    PubMed Central

    MIYATAKE, Shin-Ichi; KAWABATA, Shinji; HIRAMATSU, Ryo; KUROIWA, Toshihiko; SUZUKI, Minoru; KONDO, Natsuko; ONO, Koji

    2016-01-01

    Boron neutron capture therapy (BNCT) is a biochemically targeted radiotherapy based on the nuclear capture and fission reactions that occur when non-radioactive boron-10, which is a constituent of natural elemental boron, is irradiated with low energy thermal neutrons to yield high linear energy transfer alpha particles and recoiling lithium-7 nuclei. Therefore, BNCT enables the application of a high dose of particle radiation selectively to tumor cells in which boron-10 compound has been accumulated. We applied BNCT using nuclear reactors for 167 cases of malignant brain tumors, including recurrent malignant gliomas, newly diagnosed malignant gliomas, and recurrent high-grade meningiomas from January 2002 to May 2014. Here, we review the principle and history of BNCT. In addition, we introduce fluoride-18-labeled boronophenylalanine positron emission tomography and the clinical results of BNCT for the above-mentioned malignant brain tumors. Finally, we discuss the recent development of accelerators producing epithermal neutron beams. This development could provide an alternative to the current use of specially modified nuclear reactors as a neutron source, and could allow BNCT to be performed in a hospital setting. PMID:27250576

  20. Exploratory case-control study of brain tumors in adults

    SciTech Connect

    Burch, J.D.; Craib, K.J.; Choi, B.C.; Miller, A.B.; Risch, H.A.; Howe, G.R.

    1987-04-01

    An exploratory study of brain tumors in adults was carried out using 215 cases diagnosed in Southern Ontario between 1979 and 1982, with an individually matched, hospital control series. Significantly elevated risks were observed for reported use of spring water, drinking of wine, and consumption of pickled fish, together with a significant protective effect for the regular consumption of any of several types of fruit. While these factors are consistent with a role for N-nitroso compounds in the etiology of these tumors, for several other factors related to this hypothesis, no association was observed. Occupation in the rubber industry was associated with a significant relative risk of 9.0, though no other occupational associations were seen. Two previously unreported associations were with smoking nonfilter cigarettes with a significant trend and with the use of hair dyes or sprays. The data do not support an association between physical head trauma requiring medical attention and risk of brain tumors and indicate that exposure to ionizing radiation and vinyl chloride monomer does not contribute any appreciable fraction of attributable risk in the population studied. The findings warrant further detailed investigation in future epidemiologic studies.

  1. Boron Neutron Capture Therapy for Malignant Brain Tumors.

    PubMed

    Miyatake, Shin-Ichi; Kawabata, Shinji; Hiramatsu, Ryo; Kuroiwa, Toshihiko; Suzuki, Minoru; Kondo, Natsuko; Ono, Koji

    2016-07-15

    Boron neutron capture therapy (BNCT) is a biochemically targeted radiotherapy based on the nuclear capture and fission reactions that occur when non-radioactive boron-10, which is a constituent of natural elemental boron, is irradiated with low energy thermal neutrons to yield high linear energy transfer alpha particles and recoiling lithium-7 nuclei. Therefore, BNCT enables the application of a high dose of particle radiation selectively to tumor cells in which boron-10 compound has been accumulated. We applied BNCT using nuclear reactors for 167 cases of malignant brain tumors, including recurrent malignant gliomas, newly diagnosed malignant gliomas, and recurrent high-grade meningiomas from January 2002 to May 2014. Here, we review the principle and history of BNCT. In addition, we introduce fluoride-18-labeled boronophenylalanine positron emission tomography and the clinical results of BNCT for the above-mentioned malignant brain tumors. Finally, we discuss the recent development of accelerators producing epithermal neutron beams. This development could provide an alternative to the current use of specially modified nuclear reactors as a neutron source, and could allow BNCT to be performed in a hospital setting.

  2. Boron Neutron Capture Therapy for Malignant Brain Tumors.

    PubMed

    Miyatake, Shin-Ichi; Kawabata, Shinji; Hiramatsu, Ryo; Kuroiwa, Toshihiko; Suzuki, Minoru; Kondo, Natsuko; Ono, Koji

    2016-07-15

    Boron neutron capture therapy (BNCT) is a biochemically targeted radiotherapy based on the nuclear capture and fission reactions that occur when non-radioactive boron-10, which is a constituent of natural elemental boron, is irradiated with low energy thermal neutrons to yield high linear energy transfer alpha particles and recoiling lithium-7 nuclei. Therefore, BNCT enables the application of a high dose of particle radiation selectively to tumor cells in which boron-10 compound has been accumulated. We applied BNCT using nuclear reactors for 167 cases of malignant brain tumors, including recurrent malignant gliomas, newly diagnosed malignant gliomas, and recurrent high-grade meningiomas from January 2002 to May 2014. Here, we review the principle and history of BNCT. In addition, we introduce fluoride-18-labeled boronophenylalanine positron emission tomography and the clinical results of BNCT for the above-mentioned malignant brain tumors. Finally, we discuss the recent development of accelerators producing epithermal neutron beams. This development could provide an alternative to the current use of specially modified nuclear reactors as a neutron source, and could allow BNCT to be performed in a hospital setting. PMID:27250576

  3. Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

    PubMed Central

    Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

    2016-01-01

    Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors. PMID:27786240

  4. Round Randomized Learning Vector Quantization for Brain Tumor Imaging.

    PubMed

    Sheikh Abdullah, Siti Norul Huda; Bohani, Farah Aqilah; Nayef, Baher H; Sahran, Shahnorbanun; Al Akash, Omar; Iqbal Hussain, Rizuana; Ismail, Fuad

    2016-01-01

    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function. PMID:27516807

  5. Round Randomized Learning Vector Quantization for Brain Tumor Imaging

    PubMed Central

    2016-01-01

    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function. PMID:27516807

  6. 3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

    PubMed Central

    Zhang, Shaoting; Huang, Junzhou; Uzunbas, Mustafa; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora; Thanos, Panayotis; Metaxas, Dimitris N.

    2016-01-01

    In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance. PMID:22003750

  7. 3D segmentation of rodent brain structures using hierarchical shape priors and deformable models.

    PubMed

    Zhang, Shaoting; Huang, Junzhou; Uzunbas, Mustafa; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora; Thanos, Panayotis; Metaxas, Dimitris N

    2011-01-01

    In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance. PMID:22003750

  8. Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features.

    PubMed

    Guo, Yanrong; Wu, Guorong; Commander, Leah A; Szary, Stephanie; Jewells, Valerie; Lin, Weili; Shent, Dinggang

    2014-01-01

    Accurate segmentation of the hippocampus from infant MR brain images is a critical step for investigating early brain development. Unfortunately, the previous tools developed for adult hippocampus segmentation are not suitable for infant brain images acquired from the first year of life, which often have poor tissue contrast and variable structural patterns of early hippocampal development. From our point of view, the main problem is lack of discriminative and robust feature representations for distinguishing the hippocampus from the surrounding brain structures. Thus, instead of directly using the predefined features as popularly used in the conventional methods, we propose to learn the latent feature representations of infant MR brain images by unsupervised deep learning. Since deep learning paradigms can learn low-level features and then successfully build up more comprehensive high-level features in a layer-by-layer manner, such hierarchical feature representations can be more competitive for distinguishing the hippocampus from entire brain images. To this end, we apply Stacked Auto Encoder (SAE) to learn the deep feature representations from both T1- and T2-weighed MR images combining their complementary information, which is important for characterizing different development stages of infant brains after birth. Then, we present a sparse patch matching method for transferring hippocampus labels from multiple atlases to the new infant brain image, by using deep-learned feature representations to measure the interpatch similarity. Experimental results on 2-week-old to 9-month-old infant brain images show the effectiveness of the proposed method, especially compared to the state-of-the-art counterpart methods. PMID:25485393

  9. Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

    PubMed

    Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

    2013-10-01

    Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

  10. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

    PubMed Central

    Chen, Yunjie; Zhan, Tianming; Zhang, Ji

    2016-01-01

    We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms. PMID:27648448

  11. A unifying framework for partial volume segmentation of brain MR images.

    PubMed

    Van Leemput, Koen; Maes, Frederik; Vandermeulen, Dirk; Suetens, Paul

    2003-01-01

    Accurate brain tissue segmentation by intensity-based voxel classification of magnetic resonance (MR) images is complicated by partial volume (PV) voxels that contain a mixture of two or more tissue types. In this paper, we present a statistical framework for PV segmentation that encompasses and extends existing techniques. We start from a commonly used parametric statistical image model in which each voxel belongs to one single tissue type, and introduce an additional downsampling step that causes partial voluming along the borders between tissues. An expectation-maximization approach is used to simultaneously estimate the parameters of the resulting model and perform a PV classification. We present results on well-chosen simulated images and on real MR images of the brain, and demonstrate that the use of appropriate spatial prior knowledge not only improves the classifications, but is often indispensable for robust parameter estimation as well. We conclude that general robust PV segmentation of MR brain images requires statistical models that describe the spatial distribution of brain tissues more accurately than currently available models.

  12. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images.

    PubMed

    Chen, Yunjie; Zhan, Tianming; Zhang, Ji; Wang, Hongyuan

    2016-01-01

    We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms. PMID:27648448

  13. Segmentation of brain PET-CT images based on adaptive use of complementary information

    NASA Astrophysics Data System (ADS)

    Xia, Yong; Wen, Lingfeng; Eberl, Stefan; Fulham, Michael; Feng, Dagan

    2009-02-01

    Dual modality PET-CT imaging provides aligned anatomical (CT) and functional (PET) images in a single scanning session, which can potentially be used to improve image segmentation of PET-CT data. The ability to distinguish structures for segmentation is a function of structure and modality and varies across voxels. Thus optimal contribution of a particular modality to segmentation is spatially variant. Existing segmentation algorithms, however, seldom account for this characteristic of PET-CT data and the results using these algorithms are not optimal. In this study, we propose a relative discrimination index (RDI) to characterize the relative abilities of PET and CT to correctly classify each voxel into the correct structure for segmentation. The definition of RDI is based on the information entropy of the probability distribution of the voxel's class label. If the class label derived from CT data for a particular voxel has more certainty than that derived from PET data, the corresponding RDI will have a higher value. We applied the RDI matrix to balance adaptively the contributions of PET and CT data to segmentation of brain PET-CT images on a voxel-by-voxel basis, with the aim to give the modality with higher discriminatory power a larger weight. The resultant segmentation approach is distinguished from traditional approaches by its innovative and adaptive use of the dual-modality information. We compared our approach to the non-RDI version and two commonly used PET-only based segmentation algorithms for simulation and clinical data. Our results show that the RDI matrix markedly improved PET-CT image segmentation.

  14. Patient-specific computational biomechanics of the brain without segmentation and meshing.

    PubMed

    Zhang, Johnny Y; Joldes, Grand Roman; Wittek, Adam; Miller, Karol

    2013-02-01

    Motivated by patient-specific computational modelling in the context of image-guided brain surgery, we propose a new fuzzy mesh-free modelling framework. The method works directly on an unstructured cloud of points that do not form elements so that mesh generation is not required. Mechanical properties are assigned directly to each integration point based on fuzzy tissue classification membership functions without the need for image segmentation. Geometric integration is performed over an underlying uniform background grid. The verification example shows that, while requiring no hard segmentation and meshing, the proposed model gives, for all practical purposes, equivalent results to a finite element model. PMID:23345159

  15. Regional brain glucose metabolism in patients with brain tumors before and after radiotherapy

    SciTech Connect

    Wang, G.J.; Volkow, N.D.; Lau, Y.H.

    1994-05-01

    This study was performed to measure regional glucose metabolism in nonaffected brain regions of patients with primary or metastatic brain tumors. Seven female and four male patients (mean age 51.5{plus_minus}14.0 years old) were compared with eleven age and sex matched normal subjects. None of the patients had hydrocephalus and/or increased intracranial pressure. Brain glucose metabolism was measured using FDG-PET scan. Five of the patients were reevaluated one week after receiving radiation treatment (RT) to the brain. Patients were on Decadron and/or Dilantin at the time of both scan. PET images were analyzed with a template of 115 nonoverlapping regions of interest and then grouped into eight gray matter regions on each hemisphere. Brain regions with tumors and edema shown in MR imaging were excluded. Z scores were used to compare individual patients` regional values with those of normal subjects. The number of regional values with Z scores of less than - 3.0 were considered abnormal and were quantified. The mean global glucose metabolic rate (mean of all regions) in nonaffected brain regions of patients was significantly lower than that of normal controls (32.1{plus_minus}9.0 versus 44.8{plus_minus}6.3 {mu}mol/100g/min, p<0.001). Analyses of individual subjects revealed that none of the controls and 8 of the 11 patients had at least one abnormal region. In these 8 patients the regions which were abnormal were most frequently localized in right (n=5) and left occipital (n=6) and right orbital frontal cortex (n=7) whereas the basal ganglia was not affected. Five of the patients who had repeated scans following RT showed decrements in tumor metabolism (41{plus_minus}20.5%) and a significant increase in whole brain metabolism (8.6{plus_minus}5.3%, p<0.001). The improvement in whole brain metabolism after RT suggests that the brain metabolic decrements in the patients were related to the presence of tumoral tissue and not just a medication effect.

  16. The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

    PubMed

    Pagnozzi, Alex M; Gal, Yaniv; Boyd, Roslyn N; Fiori, Simona; Fripp, Jurgen; Rose, Stephen; Dowson, Nicholas

    2015-12-01

    Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape

  17. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.

    PubMed

    Jain, Saurabh; Sima, Diana M; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

    2015-01-01

    The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter

  18. An integrated segmentation and visualization tool for MR brain image processing

    NASA Astrophysics Data System (ADS)

    Zhuang, Audrey H.; Valentino, Daniel J.; Stambolstian, Val; Dinov, Ivo; Toga, Arthur W.

    2007-03-01

    The automated segmentation of brain structures is an important step in many neuroimaging analyses. A variety of automated segmentation tools exist, however, most segmentation results are imperfect, and require manual editing of the resulting contours or surfaces. A new, integrated segmentation and visualization tool, the LONI Anatomist, was developed to provide an open architecture for applying automated segmentation algorithms and interactive tools to manually edit the automated segmentation results. Two automated segmentation algorithms were developed to skullstrip MR brain images and were integrated in the LONI Anatomist: a two-dimensional model-based level set (2D MLS) algorithm and a three-dimensional MLS algorithm. These MLS algorithms were based on the Level Set methods by incorporating two constraints into the level set framework to evolve the zero level set surface in 2D space and 3D space respectively. In the LONI Anatomist, the evolution of the level set was displayed in real time, and final results were corrected using easy-to-use interactive editing tools. Additional tools were provided to visualize the results, such as color overlays of 2D contours over the original gray-scale slices, 3D surface visualization, etc. The LONI Anatomist was implemented in Java using a portable imaging framework (the jViewbox) for medical image display and manipulation, using the Java Image I/O plug-ins for reading/writing DICOM, MINC, ANALYZE image files, and using the Java Advanced Imaging classes for image processing. The design of the system provides a framework for researchers to integrate more mathematical algorithms for converting the algorithms into practical use.

  19. Spatial organization and correlations of cell nuclei in brain tumors.

    PubMed

    Jiao, Yang; Berman, Hal; Kiehl, Tim-Rasmus; Torquato, Salvatore

    2011-01-01

    Accepting the hypothesis that cancers are self-organizing, opportunistic systems, it is crucial to understand the collective behavior of cancer cells in their tumorous heterogeneous environment. In the present paper, we ask the following basic question: Is this self-organization of tumor evolution reflected in the manner in which malignant cells are spatially distributed in their heterogeneous environment? We employ a variety of nontrivial statistical microstructural descriptors that arise in the theory of heterogeneous media to characterize the spatial distributions of the nuclei of both benign brain white matter cells and brain glioma cells as obtained from histological images. These descriptors, which include the pair correlation function, structure factor and various nearest neighbor functions, quantify how pairs of cell nuclei are correlated in space in various ways. We map the centroids of the cell nuclei into point distributions to show that while commonly used local spatial statistics (e.g., cell areas and number of neighboring cells) cannot clearly distinguish spatial correlations in distributions of normal and abnormal cell nuclei, their salient structural features are captured very well by the aforementioned microstructural descriptors. We show that the tumorous cells pack more densely than normal cells and exhibit stronger effective repulsions between any pair of cells. Moreover, we demonstrate that brain gliomas are organized in a collective way rather than randomly on intermediate and large length scales. The existence of nontrivial spatial correlations between the abnormal cells strongly supports the view that cancer is not an unorganized collection of malignant cells but rather a complex emergent integrated system.

  20. Identifying the needs of brain tumor patients and their caregivers.

    PubMed

    Parvataneni, Rupa; Polley, Mei-Yin; Freeman, Teresa; Lamborn, Kathleen; Prados, Michael; Butowski, Nicholas; Liu, Raymond; Clarke, Jennifer; Page, Margaretta; Rabbitt, Jane; Fedoroff, Anne; Clow, Emelia; Hsieh, Emily; Kivett, Valerie; Deboer, Rebecca; Chang, Susan

    2011-09-01

    The purpose of this study is to identify the needs of brain tumor patients and their caregivers to provide improved health services to these populations. Two different questionnaires were designed for patients and caregivers. Both questionnaires contained questions pertaining to three realms: disease symptoms/treatment, health care provider, daily living/finances. The caregivers' questionnaires contained an additional domain on emotional needs. Each question was evaluated for the degree of importance and satisfaction. Exploratory analyses determined whether baseline characteristics affect responder importance or satisfaction. Also, areas of high agreement/disagreement in satisfaction between the participating patient-caregiver pairs were identified. Questions for which >50% of the patients and caregivers thought were "very important" but >30% were dissatisfied include: understanding the cause of brain tumors, dealing with patients' lower energy, identifying healthful foods and activities for patients, telephone access to health care providers, information on medical insurance coverage, and support from their employer. In the emotional realm, caregivers identified 9 out of 10 items as important but need further improvement. Areas of high disagreement in satisfaction between participating patient-caregiver pairs include: getting help with household chores (P value = 0.006) and finding time for personal needs (P value < 0.001). This study provides insights into areas to improve services for brain tumor patients and their caregivers. The caregivers' highest amount of burden is placed on their emotional needs, emphasizing the importance of providing appropriate medical and psychosocial support for caregivers to cope with emotional difficulties they face during the patients' treatment process.

  1. Statistical feature selection for enhanced detection of brain tumor

    NASA Astrophysics Data System (ADS)

    Chaddad, Ahmad; Colen, Rivka R.

    2014-09-01

    Feature-based methods are widely used in the brain tumor recognition system. Robust of early cancer detection is one of the most powerful image processing tools. Specifically, statistical features, such as geometric mean, harmonic mean, mean excluding outliers, median, percentiles, skewness and kurtosis, have been extracted from brain tumor glioma to aid in discriminating two levels namely, Level I and Level II using fluid attenuated inversion recovery (FLAIR) sequence in the diagnosis of brain tumor. Statistical feature describes the major characteristics of each level from glioma which is an important step to evaluate heterogeneity of cancer area pixels. In this paper, we address the task of feature selection to identify the relevant subset of features in the statistical domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between Level I and Level II. We apply a Decision Structure algorithm to find the optimal combination of nonhomogeneity based statistical features for the problem at hand. We employ a Naïve Bayes classifier to evaluate the performance of the optimal statistical feature based scheme in terms of its glioma Level I and Level II discrimination capability and use real-data collected from 17 patients have a glioblastoma multiforme (GBM). Dataset provided from 3 Tesla MR imaging system by MD Anderson Cancer Center. For the specific data analyzed, it is shown that the identified dominant features yield higher classification accuracy, with lower number of false alarms and missed detections, compared to the full statistical based feature set. This work has been proposed and analyzed specific GBM types which Level I and Level II and the dominant features were considered as feature aid to prognostic indicators. These features were selected automatically to be better able to determine prognosis from classical imaging studies.

  2. Brain Penetration and Efficacy of Different Mebendazole Polymorphs in a Mouse Brain Tumor Model

    PubMed Central

    Wanjiku, Teresia; Rudek, Michelle A; Joshi, Avadhut; Gallia, Gary L.; Riggins, Gregory J.

    2015-01-01

    Purpose Mebendazole (MBZ), first used as an antiparasitic drug, shows preclinical efficacy in models of glioblastoma and medulloblastoma. Three different MBZ polymorphs (A, B and C) exist and a detailed assessment of the brain penetration, pharmacokinetics and anti-tumor properties of each individual MBZ polymorph is necessary to improve mebendazole-based brain cancer therapy. Experimental Design and Results In this study, various marketed and custom-formulated MBZ tablets were analyzed for their polymorph content by IR spectroscopy and subsequently tested in orthotopic GL261 mouse glioma model for efficacy and tolerability. The pharmacokinetics and brain concentration of MBZ polymorphs and two main metabolites were analyzed by LC-MS. We found that polymorph B and C both increased survival in a GL261 glioma model, as B exhibited greater toxicity. Polymorph A showed no benefit. Both, polymorph B and C, reached concentrations in the brain that exceeded the IC50 in GL261 cells 29-fold. In addition, polymorph C demonstrated an AUC0-24h brain-to-plasma (B/P) ratio of 0.82, whereas B showed higher plasma AUC and lower B/P ratio. In contrast, polymorph A presented markedly lower levels in the plasma and brain. Furthermore, the combination with elacridar was able to significantly improve the efficacy of polymorph C in GL261 glioma and D425 medulloblastoma models in mice. Conclusion Among MBZ polymorphs, C reaches therapeutically effective concentrations in the brain tissue and tumor with less side effects and is the better choice for brain cancer therapy. Its efficacy can be further enhanced by combination with elacridar. PMID:25862759

  3. Absence of human cytomegalovirus infection in childhood brain tumors

    PubMed Central

    Sardi, Iacopo; Lucchesi, Maurizio; Becciani, Sabrina; Facchini, Ludovica; Guidi, Milena; Buccoliero, Anna Maria; Moriondo, Maria; Baroni, Gianna; Stival, Alessia; Farina, Silvia; Genitori, Lorenzo; de Martino, Maurizio

    2015-01-01

    Human cytomegalovirus (HCMV) is a common human pathogen which induces different clinical manifestations related to the age and the immune conditions of the host. HCMV infection seems to be involved in the pathogenesis of adult glioblastomas. The aim of our study was to detect the presence of HCMV in high grade gliomas and other pediatric brain tumors. This hypothesis might have important therapeutic implications, offering a new target for adjuvant therapies. Among 106 pediatric patients affected by CNS tumors we selected 27 patients with a positive HCMV serology. The serological analysis revealed 7 patients with positive HCMV IGG (≥14 U/mL), whom had also a high HCMV IgG avidity, suggesting a more than 6 months-dated infection. Furthermore, HCMV IGM were positive (≥22 U/mL) in 20 patients. Molecular and immunohistochemical analyses were performed in all the 27 samples. Despite a positive HCMV serology, confirmed by ELISA, no viral DNA was shown at the PCR analysis in the patients’ neoplastic cells. At immunohistochemistry, no expression of HCMV antigens was observed in tumoral cells. Our results are in agreement with recent results in adults which did not evidence the presence of HCMV genome in glioblastoma lesions. We did not find any correlation between HCMV infection and pediatric CNS tumors. PMID:26396923

  4. Contrast medium accumulation and washout in canine brain tumors and irradiated normal brain: a CT study of kinetics

    SciTech Connect

    Fike, J.R.; Cann, C.E.

    1984-04-01

    Kinetics of an iodinated contrast medium were evaluated quantitatively as a function of time up to one hour after intravenous infusion in the brains of dogs with experimentally induced radiation damage and dogs with spontaneous brain tumor. Radiation damage was characterized by an increase in iodine accumulation soon after the infusion, while tumor concentration of iodine either showed no change or decreased with time. These results suggest that contrast kinetic studies may be useful in differentiating radiation damage to normal brain tissue from a malignant brain tumor.

  5. Tumor treating fields: a novel treatment modality and its use in brain tumors.

    PubMed

    Hottinger, Andreas F; Pacheco, Patricia; Stupp, Roger

    2016-10-01

    Tumor treating fields (TTFields) are low-intensity electric fields alternating at an intermediate frequency (200kHz), which have been demonstrated to block cell division and interfere with organelle assembly. This novel treatment modality has shown promise in a variety of tumor types. It has been evaluated in randomized phase 3 trials in glioblastoma (GBM) and demonstrated to prolong progression-free survival (PFS) and overall survival (OS) when administered together with standard maintenance temozolomide (TMZ) chemotherapy in patients with newly diagnosed GBM. TTFields are continuously delivered by 4 transducer arrays consisting each of 9 insulated electrodes that are placed on the patient's shaved scalp and connected to a portable device. Here we summarize the preclinical data and mechanism of action, the available clinical data, and further outlook of this treatment modality in brain tumors and other cancer indications. PMID:27664860

  6. Tumor treating fields: a novel treatment modality and its use in brain tumors

    PubMed Central

    Pacheco, Patricia

    2016-01-01

    Tumor treating fields (TTFields) are low-intensity electric fields alternating at an intermediate frequency (200kHz), which have been demonstrated to block cell division and interfere with organelle assembly. This novel treatment modality has shown promise in a variety of tumor types. It has been evaluated in randomized phase 3 trials in glioblastoma (GBM) and demonstrated to prolong progression-free survival (PFS) and overall survival (OS) when administered together with standard maintenance temozolomide (TMZ) chemotherapy in patients with newly diagnosed GBM. TTFields are continuously delivered by 4 transducer arrays consisting each of 9 insulated electrodes that are placed on the patient’s shaved scalp and connected to a portable device. Here we summarize the preclinical data and mechanism of action, the available clinical data, and further outlook of this treatment modality in brain tumors and other cancer indications. PMID:27664860

  7. Three-Staged Stereotactic Radiotherapy Without Whole Brain Irradiation for Large Metastatic Brain Tumors

    SciTech Connect

    Higuchi, Yoshinori Serizawa, Toru; Nagano, Osamu; Matsuda, Shinji; Ono, Junichi; Sato, Makoto; Iwadate, Yasuo; Saeki, Naokatsu

    2009-08-01

    Purpose: To evaluate the efficacy and toxicity of staged stereotactic radiotherapy with a 2-week interfraction interval for unresectable brain metastases more than 10 cm{sup 3} in volume. Patients and Methods: Subjects included 43 patients (24 men and 19 women), ranging in age from 41 to 84 years, who had large brain metastases (> 10 cc in volume). Primary tumors were in the colon in 14 patients, lung in 12, breast in 11, and other in 6. The peripheral dose was 10 Gy in three fractions. The interval between fractions was 2 weeks. The mean tumor volume before treatment was 17.6 {+-} 6.3 cm{sup 3} (mean {+-} SD). Mean follow-up interval was 7.8 months. The local tumor control rate, as well as overall, neurological, and qualitative survivals, were calculated using the Kaplan-Meier method. Results: At the time of the second and third fractions, mean tumor volumes were 14.3 {+-} 6.5 (18.8% reduction) and 10.6 {+-} 6.1 cm{sup 3} (39.8% reduction), respectively, showing significant reductions. The median overall survival period was 8.8 months. Neurological and qualitative survivals at 12 months were 81.8% and 76.2%, respectively. Local tumor control rates were 89.8% and 75.9% at 6 and 12 months, respectively. Tumor recurrence-free and symptomatic edema-free rates at 12 months were 80.7% and 84.4%, respectively. Conclusions: The 2-week interval allowed significant reduction of the treatment volume. Our results suggest staged stereotactic radiotherapy using our protocol to be a possible alternative for treating large brain metastases.

  8. Tumor Directed, Scalp Sparing Intensity Modulated Whole Brain Radiotherapy for Brain Metastases.

    PubMed

    Kao, Johnny; Darakchiev, Boramir; Conboy, Linda; Ogurek, Sara; Sharma, Neha; Ren, Xuemin; Pettit, Jeffrey

    2015-10-01

    Despite significant technical advances in radiation delivery, conventional whole brain radiation therapy (WBRT) has not materially changed in the past 50 years. We hypothesized that IMRT can selectively spare uninvolved brain and scalp with the goal of reducing acute and late toxicity. MRI/CT simulation image registration was performed. We performed IMRT planning to simultaneously treat the brain tumor(s) on MRI + 5 mm margin to 37.5 Gy in 15 fractions while limiting the uninvolved brain + 2 mm margin to 30 Gy in 15 fractions and the mean scalp dose to #18 Gy. Three field IMRT plans were compared to conventional WBRT plans. Symptomatic patients were started on conventional WBRT for 2 to 3 fractions while IMRT planning was performed. Seventeen consecutive patients with brain metastases with RPA class I and II disease with no leptomeningeal spread were treated with IMRT WBRT. Compared to conventional WBRT, IMRT reduced the mean scalp dose (26.2 Gy vs. 16.4 Gy, p < 0.001) and the mean PTV30 dose (38.4 Gy vs. 32.0 Gy, p < 0.001) while achieving similar mean PTV37.5 doses (38.3 Gy vs. 38.0 Gy, p = 0.26). Using Olsen hair loss score criteria, 4 of 15 assessable patients preserved at least 50% of hair coverage at 1 to 3 months after treatment while 6 patients preserved between 25 and 50% hair coverage. At a median follow-up of 6.8 months (range: 5 to 15 months), the median overall survival was 5.4 months. Four patients relapsed within the brain, one within the PTV37.5 and three outside the PTV37.5. Tumor directed, scalp sparing IMRT is feasible, achieves rational dose distributions and preserves partial hair coverage in the majority of patients. Further studies are warranted to determine whether the increased utilization of resources needed for IMRT are appropriate in this setting.

  9. Bystander effect-mediated therapy of experimental brain tumor by genetically engineered tumor cells.

    PubMed

    Namba, H; Tagawa, M; Iwadate, Y; Kimura, M; Sueyoshi, K; Sakiyama, S

    1998-01-01

    Transfer of the herpes simplex virus-thymidine kinase (HSV-tk) gene, followed by administration of ganciclovir (GCV), generates the "bystander effect," in which HSV-tk-negative wild-type cells, as well as HSV-tk-expressing cells, are killed by GCV. To eradicate an intracranial tumor by this bystander effect, we injected the tumor cells transduced with the HSV-tk gene (TK cells) in the vicinity of the preimplanted wild-type tumor and then administered GCV. Wild-type 9L-gliosarcoma cells (1 x 10[5]) were implanted into the brain of syngeneic Fisher rats. On the next day, rats were injected with TK cells (1 x 10(5) or 3 x 10[5]) or medium alone at the same brain coordinate and then treated with GCV or saline. Administration of GCV significantly prolonged the survival of the rats injected with TK cells compared with that injected with medium alone (p < 0.01). Reduction in tumor size and retardation of tumor growth were observed by serial magnetic resonance imaging in the rats that received the combination of TK cells and GCV. The results show that the bystander effect is also achieved in vivo even when TK cells and wild-type cells are not simultaneously implanted. This treatment modality circumvents potential risks accompanied with in vivo gene transfer. Because there remained substantially no HSV-tk-positive cells in the recurrent tumors, this modality offers a "safe" therapeutic strategy against human malignant gliomas. PMID:9458237

  10. Multimodality 3D Tumor Segmentation in HCC patients treated with TACE

    PubMed Central

    Wang, Zhijun; Chapiro, Julius; Schernthaner, Rüdiger; Duran, Rafael; Chen, Rongxin; Geschwind, Jean-François; Lin, MingDe

    2015-01-01

    Rationale and Objectives To validate the concordance of a semi-automated multimodality lesion segmentation technique between contrast-enhanced MRI (CE-MRI), cone-beam CT (CBCT) and multi-detector CT (MDCT) in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Materials and methods This retrospective analysis included 45 patients with unresectable HCC that underwent baseline CE-MRI within one month before the treatment, intraprocedural CBCT during conventional TACE and MDCT within 24 hours post TACE. Fourteen patients were excluded due to atypical lesion morphology, portal vein invasion or small lesion size which precluded sufficient lesion visualization. 31 patients with a total of 40 target lesions were included into the analysis. A tumor segmentation software, based on non-Euclidean geometry and theory of radial basis functions, was used to allow for the segmentation of target lesions in 3D on all three modalities. The algorithm created image-based masks located in a 3D region whose center and size was defined by the user, yielding the nomenclature “semi-automatic”. Based on that, tumor volumes on all three modalities were calculated and compared using a linear regression model (R2 values). Residual plots were used to analyze drift and variance of the values. Results The mean value of tumor volumes was 18.72±19.13cm3 (range, 0.41-59.16cm3) on CE-MRI, 21.26±21.99 cm3 (range, 0.62-86.82 cm3) on CBCT and 19.88±20.88 cm3 (range, 0.45-75.24 cm3) on MDCT. The average volumes of the tumor were not significantly different between CE-MR and DP-CBCT, DP-CBCT and MDCT, MDCT and CE-MR (p=0.577, 0.770 and 0.794 respectively). A strong correlation between volumes on CE-MRI and CBCT, CBCT and MDCT, MDCT and CE-MRI was observed (R2=0.974, 0.992 and 0.983, respectively). When plotting the residuals, no drift was observed for all methods showing deviations of no more than 10% of absolute volumes (in cm3). Conclusion A semi

  11. The modern brain tumor operating room: from standard essentials to current state-of-the-art.

    PubMed

    Barnett, Gene H; Nathoo, Narendra

    2004-01-01

    It is just over a century since successful brain tumor resection. Since then the diagnosis, imaging, and management of brain tumors have improved, in large part due to technological advances. Similarly, the operating room (OR) for brain tumor surgery has increased in complexity and specificity with multiple forms of equipment now considered necessary as technical adjuncts. It is evident that the theme of minimalism in combination with advanced image-guidance techniques and a cohort of sophisticated technologies (e.g., robotics and nanotechnology) will drive changes in the current OR environment for the foreseeable future. In this report we describe what may be regarded today as standard essentials in an operating room for the surgical management of brain tumors and what we believe to be the current 'state-of-the-art' brain tumor OR. Also, we speculate on the additional capabilities of the brain tumor OR of the near future. PMID:15527078

  12. Blood-tissue barrier of human brain tumors: correlation of scintigraphic and ultrastructural finding: concise communication

    SciTech Connect

    Front, D.; Israel, O.; Kohn, S.; Nir, I.

    1984-04-01

    Through the first 2 hr, uptake of (Tc-99m)pertechnetate and of Co-57 bleomycin were assessed in 29 brain tumors and were correlated with the ultrastructure of the tumor's capillary endothelium. No difference in uptake was found between the two tracers. Permeability of brain tumors to these agents was found to be governed by the same ultrastructural features that determine permeability in experimental brain tumors: the type of junction between contiguous endothelial cells in the capillaries. That uptake of (Tc-99m)pertechnetate and of Co-57 bleomycin depends on tumor capillary ultrastructure (which determines the permeability) suggests the possibility of the use of radiopharmaceuticals as in vivo indicators of tumor permeability. Brain scintigraphy may help to assess brain-tumor availability to non-lipid-soluble chemotherapeutic drugs.

  13. [ANALYSIS OF COMPLICATIONS POSTOPERATIVE CAUSES AND MORTALITY AFTER RADICAL TREATMENT FOR TUMORS OF THE LEFT ANATOMICAL SEGMENT OF THE PANCREAS].

    PubMed

    Kopchak, V M; Kopchak, K V; Khomyak, I V; Duvalko, O V; Tkachuk, O S; Andronik, S V; Shevkolenko, H H; Khanenko, V V; Kvasivka, O O; Zubkov, O O

    2015-07-01

    Radical surgery for tumors of the left anatomical and surgical segment of the pancreas proved for distal resection in various versions, central resection and enucleation of tumors. The causes of early postoperative complications and mortality in 129 patients aged from 14 to 81 years, operated on for neoplastic lesions of the left anatomical segment of the pancreas in the period from 2009 to 2014 were analysed. The influence of various factors of risk of complications and mortality were studied in particular, extended resection, for tumor invasion of adjacent organs, and adjacent vessels.

  14. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy.

    PubMed

    Ji, Minbiao; Lewis, Spencer; Camelo-Piragua, Sandra; Ramkissoon, Shakti H; Snuderl, Matija; Venneti, Sriram; Fisher-Hubbard, Amanda; Garrard, Mia; Fu, Dan; Wang, Anthony C; Heth, Jason A; Maher, Cormac O; Sanai, Nader; Johnson, Timothy D; Freudiger, Christian W; Sagher, Oren; Xie, Xiaoliang Sunney; Orringer, Daniel A

    2015-10-14

    Differentiating tumor from normal brain is a major barrier to achieving optimal outcome in brain tumor surgery. New imaging techniques for visualizing tumor margins during surgery are needed to improve surgical results. We recently demonstrated the ability of stimulated Raman scattering (SRS) microscopy, a nondestructive, label-free optical method, to reveal glioma infiltration in animal models. We show that SRS reveals human brain tumor infiltration in fresh, unprocessed surgical specimens from 22 neurosurgical patients. SRS detects tumor infiltration in near-perfect agreement with standard hematoxylin and eosin light microscopy (κ = 0.86). The unique chemical contrast specific to SRS microscopy enables tumor detection by revealing quantifiable alterations in tissue cellularity, axonal density, and protein/lipid ratio in tumor-infiltrated tissues. To ensure that SRS microscopic data can be easily used in brain tumor surgery, without the need for expert interpretation, we created a classifier based on cellularity, axonal density, and protein/lipid ratio in SRS images capable of detecting tumor infiltration with 97.5% sensitivity and 98.5% specificity. Quantitative SRS microscopy detects the spread of tumor cells, even in brain tissue surrounding a tumor that appears grossly normal. By accurately revealing tumor infiltration, quantitative SRS microscopy holds potential for improving the accuracy of brain tumor surgery.

  15. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy

    PubMed Central

    Ji, Minbiao; Lewis, Spencer; Camelo-Piragua, Sandra; Ramkissoon, Shakti H.; Snuderl, Matija; Venneti, Sriram; Fisher-Hubbard, Amanda; Garrard, Mia; Fu, Dan; Wang, Anthony C.; Heth, Jason A.; Maher, Cormac O.; Sanai, Nader; Johnson, Timothy D.; Freudiger, Christian W.; Sagher, Oren; Xie, Xiaoliang Sunney; Orringer, Daniel A.

    2016-01-01

    Differentiating tumor from normal brain is a major barrier to achieving optimal outcome in brain tumor surgery. New imaging techniques for visualizing tumor margins during surgery are needed to improve surgical results. We recently demonstrated the ability of stimulated Raman scattering (SRS) microscopy, a non-destructive, label-free optical method, to reveal glioma infiltration in animal models. Here we show that SRS reveals human brain tumor infiltration in fresh, unprocessed surgical specimens from 22 neurosurgical patients. SRS detects tumor infiltration in near-perfect agreement with standard hematoxylin and eosin light microscopy (κ=0.86). The unique chemical contrast specific to SRS microscopy enables tumor detection by revealing quantifiable alterations in tissue cellularity, axonal density and protein:lipid ratio in tumor-infiltrated tissues. To ensure that SRS microscopic data can be easily used in brain tumor surgery, without the need for expert interpretation, we created a classifier based on cellularity, axonal density and protein:lipid ratio in SRS images capable of detecting tumor infiltration with 97.5% sensitivity and 98.5% specificity. Importantly, quantitative SRS microscopy detects the spread of tumor cells, even in brain tissue surrounding a tumor that appears grossly normal. By accurately revealing tumor infiltration, quantitative SRS microscopy holds potential for improving the accuracy of brain tumor surgery. PMID:26468325

  16. Consensus Conference on Brain Tumor Definition for registration. November 10, 2000.

    PubMed Central

    McCarthy, Bridget J.; Surawicz, Tanya; Bruner, Janet M.; Kruchko, Carol; Davis, Faith

    2002-01-01

    The Consensus Conference on Brain Tumor Definition was facilitated by the Central Brain Tumor Registry of the United States and held on November 10, 2000, in Chicago, Illinois, to reach multidisciplinary agreement on a standard definition of brain tumors for collecting and comparing data in the U.S. The Brain Tumor Working Group, convened in 1998 to determine the status of brain tumor collection in the U.S., outlined 4 recommendations of which the first 2 guided the discussion for the Consensus Conference: (1) standardization of a definition of primary brain tumors that is based on site alone, rather than on site and behavior, and that can be used by surveillance organizations in collecting these tumors; and (2) development of a reporting scheme that can be used for comparing estimates of primary brain tumors across registries. Consensus was reached on the collection of all primary brain tumor histologies found and reported in the brain or CNS ICD-O site codes (C70.0-C72.9 and C75.1-C75.3), including those coded benign and uncertain as well as those coded malignant. In addition, a comprehensive listing of histologies occurring in the brain and CNS, based on the CBTRUS grouping scheme, was formulated to provide a template for reporting in accordance with the second recommendation of the Brain Tumor Working Group. With consensus achieved on the first 2 recommendations, the stage is set to move forward in estimating additional resources necessary for the collection of these tumors, including funding, training for cancer registrars, identifying quality control measures, and developing computerized edit checks, as outlined in the last 2 recommendations of the Brain Tumor Working Group. PMID:11916506

  17. Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation

    PubMed Central

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S.; Prince, Jerry L.; Pham, Dzung L.

    2015-01-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available, state-of-the art approaches. PMID:26340685

  18. Three validation metrics for automated probabilistic image segmentation of brain tumours

    PubMed Central

    Zou, Kelly H.; Wells, William M.; Kikinis, Ron; Warfield, Simon K.

    2005-01-01

    SUMMARY The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts’ manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered. PMID:15083482

  19. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    PubMed

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  20. What's New in Research and Treatment for Brain Tumors in Children?

    MedlinePlus

    ... brain and spinal cord tumors in children What’s new in research and treatment for brain and spinal ... an investigational method, and studies are continuing. Other new treatment strategies Researchers are also testing some newer ...

  1. Analgesic use and the risk of primary adult brain tumor.

    PubMed

    Egan, Kathleen M; Nabors, Louis B; Thompson, Zachary J; Rozmeski, Carrie M; Anic, Gabriella A; Olson, Jeffrey J; LaRocca, Renato V; Chowdhary, Sajeel A; Forsyth, Peter A; Thompson, Reid C

    2016-09-01

    Glioma and meningioma are uncommon tumors of the brain with few known risk factors. Regular use of aspirin has been linked to a lower risk of gastrointestinal and other cancers, though evidence for an association with brain tumors is mixed. We examined the association of aspirin and other analgesics with the risk of glioma and meningioma in a large US case-control study. Cases were persons recently diagnosed with glioma or meningioma and treated at medical centers in the southeastern US. Controls were persons sampled from the same communities as the cases combined with friends and other associates of the cases. Information on past use of analgesics (aspirin, other anti-inflammatory agents, and acetaminophen) was collected in structured interviews. Logistic regression was used to estimate odds ratios (ORs) and 95 % confidence intervals (CIs) for analgesic use adjusted for potential confounders. All associations were considered according to indication for use. A total of 1123 glioma cases, 310 meningioma cases and 1296 controls were included in the analysis. For indications other than headache, glioma cases were less likely than controls to report regular use of aspirin (OR 0.69; CI 0.56, 0.87), in a dose-dependent manner (P trend < 0.001). No significant associations were observed with other analgesics for glioma, or any class of pain reliever for meningioma. Results suggest that regular aspirin use may reduce incidence of glioma. PMID:26894804

  2. Gene Therapy and Virotherapy: Novel Therapeutic Approaches for Brain Tumors

    PubMed Central

    Kroeger, Kurt M.; Ghulam Muhammad, A.K.M.; Baker, Gregory J.; Assi, Hikmat; Wibowo, Mia K.; Xiong, Weidong; Yagiz, Kader; Candolfi, Marianela; Lowenstein, Pedro R.; Castro, Maria G.

    2010-01-01

    Glioblastoma multiforme (GBM) is a deadly primary brain tumor in adults, with a median survival of ~12–18 months post-diagnosis. Despite recent advances in conventional therapeutic approaches, only modest improvements in median survival have been achieved; GBM usually recurs within 12 months post-resection, with poor prognosis. Thus, novel therapeutic strategies to target and kill GBM cells are desperately needed. Our group and others are pursuing virotherapy and gene therapy strategies for the treatment of GBM. In this review, we will discuss various virotherapy and gene therapy approaches for GBM currently under preclinical and clinical evaluation including direct or conditional cytotoxic, and/or immunostimulatory approaches. We also discuss cutting-edge technologies for drug/gene delivery and targeting brain tumors, including the use of stem cells as delivery platforms, the use of targeted immunotoxins, and the therapeutic potential of using GBM microvesicles to deliver therapeutic siRNAs or virotherapies. Finally, various animal models available to test novel GBM therapies are discussed. PMID:21034670

  3. Is outpatient brain tumor surgery feasible in India?

    PubMed

    Turel, Mazda K; Bernstein, Mark

    2016-01-01

    The current trend in all fields of surgery is towards less invasive procedures with shorter hospital stays. The reasons for this change include convenience to patients, optimal resource utilization, and cost saving. Technological advances in neurosurgery, aided by improvements in anesthesia, have resulted in surgery that is faster, simpler, and safer with excellent perioperative recovery. As a result of improved outcomes, some centers are performing brain tumor surgery on an outpatient basis, wherein patients arrive at the hospital the morning of their procedure and leave the hospital the same evening, thus avoiding an overnight stay in the hospital. In addition to the medical benefits of the outpatient procedure, its impact on patient satisfaction is substantial. The economic benefits are extremely favorable for the patient, physician, as well as the hospital. In high volume centers, a day surgery program can exist alongside those for elective and emergency surgeries, providing another pathway for patient care. However, due to skepticism surrounding the medicolegal aspects, and how radical the concept at first sounds, these procedures have not gained widespread popularity. We provide an overview of outpatient brain tumor surgery in the western world, discussing the socioeconomic, medicolegal, and ethical issues related to its adaptability in a developing nation. PMID:27625225

  4. Focal Segmental Glomerulosclerosis Secondary to Juxtaglomerular Cell Tumor during Pregnancy: A Case Report

    PubMed Central

    Ohashi, Yasushi; Kobayashi, Shizuka; Arai, Taichi; Nemoto, Tetsuo; Aoki, Chizu; Nagata, Masato; Sakai, Ken

    2014-01-01

    Juxtaglomerular cell tumor is a rare renal neoplasm. Secondary hypertension with juxtaglomerular cell tumor can be seen in females in their 20s and 30s. We present a case of juxtaglomerular cell tumor during pregnancy. A 32-year-old female was hospitalized for refractory hypertension and nephrotic syndrome in the 23rd gestational week. One year before admission, she had been diagnosed with hypertension; plasma renin activity at that time had been 2.3 ng/ml/h. Her blood pressure was uncontrolled during pregnancy, and proteinuria was detected in the 12th gestational week despite the administration of antihypertensive medications. Laboratory data showed proteinuria, hypokalemia, and hypoalbuminemia. In the 25th gestational week, she underwent surgical termination of the pregnancy because of congestive heart failure and acute renal injury. After the termination of the pregnancy and the delivery of a viable fetus, her hypertension and nephrotic syndrome were found to persist with a high plasma renin activity (13 ng/ml/h). Ultrasonography showed a 5.5-cm left renal cystic mass with a partially solid component at the lower renal pole. The left kidney with the renal mass was excised by laparoscopic nephrectomy. Plasma renin activity normalized the next day, with a decrease in blood pressure to 120–130/80–90 mm Hg; however, proteinuria remained at ≥3.5 g/day. On the basis of histopathological findings, the patient was diagnosed with a juxtaglomerular cell tumor and focal segmental glomerulosclerosis. Juxtaglomerular cell tumor is a rare renin-secreting tumor associated with refractory hypertension in young females and is a possible cause of hypertension during pregnancy. PMID:24926309

  5. Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

    2014-03-01

    Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

  6. Gliomatosis cerebri: no evidence for a separate brain tumor entity.

    PubMed

    Herrlinger, Ulrich; Jones, David T W; Glas, Martin; Hattingen, Elke; Gramatzki, Dorothee; Stuplich, Moritz; Felsberg, Jörg; Bähr, Oliver; Gielen, Gerrit H; Simon, Matthias; Wiewrodt, Dorothee; Schabet, Martin; Hovestadt, Volker; Capper, David; Steinbach, Joachim P; von Deimling, Andreas; Lichter, Peter; Pfister, Stefan M; Weller, Michael; Reifenberger, Guido

    2016-02-01

    Gliomatosis cerebri (GC) is presently considered a distinct astrocytic glioma entity according to the WHO classification for CNS tumors. It is characterized by widespread, typically bilateral infiltration of the brain involving three or more lobes. Genetic studies of GC have to date been restricted to the analysis of individual glioma-associated genes, which revealed mutations in the isocitrate dehydrogenase 1 (IDH1) and tumor protein p53 (TP53) genes in subsets of patients. Here, we report on a genome-wide analysis of DNA methylation and copy number aberrations in 25 GC patients. Results were compared with those obtained for 105 patients with various types of conventional, i.e., non-GC gliomas including diffuse astrocytic gliomas, oligodendrogliomas and glioblastomas. In addition, we assessed the prognostic role of methylation profiles and recurrent DNA copy number aberrations in GC patients. Our data reveal that the methylation profiles in 23 of the 25 GC tumors corresponded to either IDH mutant astrocytoma (n = 6), IDH mutant and 1p/19q codeleted oligodendroglioma (n = 5), or IDH wild-type glioblastoma including various molecular subgroups, i.e., H3F3A-G34 mutant (n = 1), receptor tyrosine kinase 1 (RTK1, n = 4), receptor tyrosine kinase 2 (classic) (RTK2, n = 2) or mesenchymal (n = 5) glioblastoma groups. Two tumors showed methylation profiles of normal brain tissue due to low tumor cell content. While histological grading (WHO grade IV vs. WHO grade II and III) was not prognostic, the molecular classification as classic/RTK2 or mesenchymal glioblastoma was associated with worse overall survival. Multivariate Cox regression analysis revealed MGMT promoter methylation as a positive prognostic factor. Taken together, DNA-based large-scale molecular profiling indicates that GC comprises a genetically and epigenetically heterogeneous group of diffuse gliomas that carry DNA methylation and copy number profiles closely matching the common molecularly

  7. The Role of Fast Cell Cycle Analysis in Pediatric Brain Tumors.

    PubMed

    Alexiou, George A; Vartholomatos, George; Stefanaki, Kalliopi; Lykoudis, Efstathios G; Patereli, Amalia; Tseka, Georgia; Tzoufi, Meropi; Sfakianos, George; Prodromou, Neofytos

    2015-01-01

    Cell cycle analysis by flow cytometry has not been adequately studied in pediatric brain tumors. We investigated the value of a modified rapid (within 6 min) cell cycle analysis protocol for the characterization of malignancy of pediatric brain tumors and for the differentiation of neoplastic from nonneoplastic tissue for possible intraoperative application. We retrospectively studied brain tumor specimens from patients treated at our institute over a 5-year period. All tumor samples were histopathologically verified before flow-cytometric analysis. The histopathological examination of permanent tissue sections was the gold standard. There were 68 brain tumor cases. All tumors had significantly lower G0/G1 and significantly higher S phase and mitosis fractions than normal brain tissue. Furthermore low-grade tumors could be differentiated from high-grade tumors. DNA aneuploidy was detected in 35 tumors. A correlation between S phase fraction and Ki-67 index was found in medulloblastomas and anaplastic ependymomas. Rapid cell cycle analysis by flow cytometry is a promising method for the identification of neoplastic tissue intraoperatively. Low-grade tumors could be differentiated from high-grade tumors. Thus, cell cycle analysis can be a valuable adjunct to the histopathological evaluation of pediatric brain tumors, whereas its intraoperative application warrants further investigation. PMID:26287721

  8. Intraoperative brain tumor resection cavity characterization with conoscopic holography

    NASA Astrophysics Data System (ADS)

    Simpson, Amber L.; Burgner, Jessica; Chen, Ishita; Pheiffer, Thomas S.; Sun, Kay; Thompson, Reid C.; Webster, Robert J., III; Miga, Michael I.

    2012-02-01

    Brain shift compromises the accuracy of neurosurgical image-guided interventions if not corrected by either intraoperative imaging or computational modeling. The latter requires intraoperative sparse measurements for constraining and driving model-based compensation strategies. Conoscopic holography, an interferometric technique that measures the distance of a laser light illuminated surface point from a fixed laser source, was recently proposed for non-contact surface data acquisition in image-guided surgery and is used here for validation of our modeling strategies. In this contribution, we use this inexpensive, hand-held conoscopic holography device for intraoperative validation of our computational modeling approach to correcting for brain shift. Laser range scan, instrument swabbing, and conoscopic holography data sets were collected from two patients undergoing brain tumor resection therapy at Vanderbilt University Medical Center. The results of our study indicate that conoscopic holography is a promising method for surface acquisition since it requires no contact with delicate tissues and can characterize the extents of structures within confined spaces. We demonstrate that for two clinical cases, the acquired conoprobe points align with our model-updated images better than the uncorrected images lending further evidence that computational modeling approaches improve the accuracy of image-guided surgical interventions in the presence of soft tissue deformations.

  9. SEMI-AUTOMATIC SEGMENTATION OF BRAIN SUBCORTICAL STRUCTURES FROM HIGH-FIELD MRI

    PubMed Central

    Kim, Jinyoung; Lenglet, Christophe; Sapiro, Guillermo; Harel, Noam

    2015-01-01

    Volumetric segmentation of subcortical structures such as the basal ganglia and thalamus is necessary for non-invasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. This paper presents a semi-automatic segmentation system exploiting the superior image quality of ultra-high field (7 Tesla) MRI. The proposed approach handles and exploits multiple structural MRI modalities. It uniquely combines T1-weighted (T1W), T2-weighted (T2W), diffusion, and susceptibility-weighted (SWI) MRI and introduces a dedicated new edge indicator function. In addition to this, we employ prior shape and configuration knowledge of the subcortical structures in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining over-segmentation at their borders with a non-overlapping penalty. Extensive experiments with data acquired on a 7T MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as deep brain stimulation. PMID:25192576

  10. Using Ferumoxytol-Enhanced MRI to Measure Inflammation in Patients With Brain Tumors or Other Conditions of the CNS

    ClinicalTrials.gov

    2016-07-08

    Brain Injury; Central Nervous System Degenerative Disorder; Central Nervous System Infectious Disorder; Central Nervous System Vascular Malformation; Hemorrhagic Cerebrovascular Accident; Ischemic Cerebrovascular Accident; Primary Brain Neoplasm; Brain Cancer; Brain Tumors

  11. Fetal autonomic brain age scores, segmented heart rate variability analysis, and traditional short term variability

    PubMed Central

    Hoyer, Dirk; Kowalski, Eva-Maria; Schmidt, Alexander; Tetschke, Florian; Nowack, Samuel; Rudolph, Anja; Wallwitz, Ulrike; Kynass, Isabelle; Bode, Franziska; Tegtmeyer, Janine; Kumm, Kathrin; Moraru, Liviu; Götz, Theresa; Haueisen, Jens; Witte, Otto W.; Schleußner, Ekkehard; Schneider, Uwe

    2014-01-01

    Disturbances of fetal autonomic brain development can be evaluated from fetal heart rate patterns (HRP) reflecting the activity of the autonomic nervous system. Although HRP analysis from cardiotocographic (CTG) recordings is established for fetal surveillance, temporal resolution is low. Fetal magnetocardiography (MCG), however, provides stable continuous recordings at a higher temporal resolution combined with a more precise heart rate variability (HRV) analysis. A direct comparison of CTG and MCG based HRV analysis is pending. The aims of the present study are: (i) to compare the fetal maturation age predicting value of the MCG based fetal Autonomic Brain Age Score (fABAS) approach with that of CTG based Dawes-Redman methodology; and (ii) to elaborate fABAS methodology by segmentation according to fetal behavioral states and HRP. We investigated MCG recordings from 418 normal fetuses, aged between 21 and 40 weeks of gestation. In linear regression models we obtained an age predicting value of CTG compatible short term variability (STV) of R2 = 0.200 (coefficient of determination) in contrast to MCG/fABAS related multivariate models with R2 = 0.648 in 30 min recordings, R2 = 0.610 in active sleep segments of 10 min, and R2 = 0.626 in quiet sleep segments of 10 min. Additionally segmented analysis under particular exclusion of accelerations (AC) and decelerations (DC) in quiet sleep resulted in a novel multivariate model with R2 = 0.706. According to our results, fMCG based fABAS may provide a promising tool for the estimation of fetal autonomic brain age. Beside other traditional and novel HRV indices as possible indicators of developmental disturbances, the establishment of a fABAS score normogram may represent a specific reference. The present results are intended to contribute to further exploration and validation using independent data sets and multicenter research structures. PMID:25505399

  12. Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

    PubMed

    Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

    2012-01-01

    Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. PMID:21719257

  13. Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

    PubMed

    Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

    2012-01-01

    Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images.

  14. Brain Magnetic Resonance Imaging After High-Dose Chemotherapy and Radiotherapy for Childhood Brain Tumors

    SciTech Connect

    Spreafico, Filippo Gandola, Lorenza; Marchiano, Alfonso; Simonetti, Fabio; Poggi, Geraldina; Adduci, Anna; Clerici, Carlo Alfredo; Luksch, Roberto; Biassoni, Veronica; Meazza, Cristina; Catania, Serena; Terenziani, Monica; Musumeci, Renato; Fossati-Bellani, Franca; Massimino, Maura

    2008-03-15

    Purpose: Brain necrosis or other subacute iatrogenic reactions has been recognized as a potential complication of radiotherapy (RT), although the possible synergistic effects of high-dose chemotherapy and RT might have been underestimated. Methods and Materials: We reviewed the clinical and radiologic data of 49 consecutive children with malignant brain tumors treated with high-dose thiotepa and autologous hematopoietic stem cell rescue, preceded or followed by RT. The patients were assessed for neurocognitive tests to identify any correlation with magnetic resonance imaging (MRI) anomalies. Results: Of the 49 children, 18 (6 of 25 with high-grade gliomas and 12 of 24 with primitive neuroectodermal tumors) had abnormal brain MRI findings occurring a median of 8 months (range, 2-39 months) after RT and beginning to regress a median of 13 months (range, 2-26 months) after onset. The most common lesion pattern involved multiple pseudonodular, millimeter-size, T{sub 1}-weighted unevenly enhancing, and T{sub 2}-weighted hyperintense foci. Four patients with primitive neuroectodermal tumors also had subdural fluid leaks, with meningeal enhancement over the effusion. One-half of the patients had symptoms relating to the new radiographic findings. The MRI lesion-free survival rate was 74% {+-} 6% at 1 year and 57% {+-} 8% at 2 years. The number of marrow ablative courses correlated significantly to the incidence of radiographic anomalies. No significant difference was found in intelligent quotient scores between children with and without radiographic changes. Conclusion: Multiple enhancing cerebral lesions were frequently seen on MRI scans soon after high-dose chemotherapy and RT. Such findings pose a major diagnostic challenge in terms of their differential diagnosis vis-a-vis recurrent tumor. Their correlation with neurocognitive results deserves further investigation.

  15. WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

    SciTech Connect

    Gensheimer, M; Trister, A; Ermoian, R; Hawkins, D

    2014-06-15

    Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was

  16. The genesis of peritumoral vasogenic brain edema and tumor cysts: a hypothetical role for tumor-derived vascular permeability factor.

    PubMed Central

    Criscuolo, G. R.

    1993-01-01

    Cerebral edema and fluid-filled cysts are common accompaniments of brain tumors. They contribute to the mass effect imposed by the primary tumor and are often responsible for a patient's signs and symptoms. Cerebral edema significantly increases the morbidity associated with tumor biopsy, excision, radiation therapy, and chemotherapy. Both edema and cyst formation are thought to result from a deficiency in the blood-brain barrier, with consequent extravasation of water, electrolytes, and plasma proteins from altered tumor microvessels. The resultant expansion of the cerebral interstitial space contributes to the elevated intracranial pressure observed with brain tumors. Departure from the typical blood-brain barrier microvascular architecture may only partially explain the occurrence of edema and tumor cyst formation. Biochemical mediators have also been implicated in vascular extravasation. Vascular permeability factor or vascular endothelial growth factor (VPF/VEGF) is a protein that has recently been isolated from a variety of tumors including human brain tumors. VPFb is an extraordinarily potent inducer of both microvascular extravasation (edemagenesis) and the formation of new blood vessels (angiogenesis). Its role in tumor growth and progression would therefore appear pivotal. Herein, the author presents an updated account of the investigation of VPF. Historical and clinical perspectives of the study and treatment of tumor associated edema are provided. The efficacy of high-dose dexamethasone in the treatment of neoplastic brain edema is discussed. A hypothetical role for VPF in edemagenesis is presented and discussed. It is hoped that an expanded understanding of the mechanisms responsible for the genesis of edema will ultimately facilitate therapeutic intervention. Images Figure 1 Figure 2 Figure 3 PMID:7516104

  17. Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images.

    PubMed

    Ju, Wei; Xiang, Dehui; Xiang, Deihui; Zhang, Bin; Wang, Lirong; Kopriva, Ivica; Chen, Xinjian

    2015-12-01

    Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images and low contrast in computed tomography (CT) images, segmentation of tumor in the PET and CT images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on the PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph, including two sub-graphs and a special link, is constructed, in which one sub-graph is for the PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For the PET, a downhill cost and a 3D derivative cost are proposed. For the CT, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 PET-CT images. The experimental results indicate that the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph cut method.

  18. Significant predictors of patients' uncertainty in primary brain tumors.

    PubMed

    Lin, Lin; Chien, Lung-Chang; Acquaye, Alvina A; Vera-Bolanos, Elizabeth; Gilbert, Mark R; Armstrong, Terri S

    2015-05-01

    Patients with primary brain tumors (PBT) face uncertainty related to prognosis, symptoms and treatment response and toxicity. Uncertainty is correlated to negative mood states and symptom severity and interference. This study identified predictors of uncertainty during different treatment stages (newly-diagnosed, on treatment, followed-up without active treatment). One hundred eighty six patients with PBT were accrued at various points in the illness trajectory. Data collection tools included: a clinical checklist/a demographic data sheet/the Mishel Uncertainty in Illness Scale-Brain Tumor Form. The structured additive regression model was used to identify significant demographic and clinical predictors of illness-related uncertainty. Participants were primarily white (80 %) males (53 %). They ranged in age from 19-80 (mean = 44.2 ± 12.6). Thirty-two of the 186 patients were newly-diagnosed, 64 were on treatment at the time of clinical visit with MRI evaluation, 21 were without MRI, and 69 were not on active treatment. Three subscales (ambiguity/inconsistency; unpredictability-disease prognoses; unpredictability-symptoms and other triggers) were different amongst the treatment groups (P < .01). However, patients' uncertainty during active treatment was as high as in newly-diagnosed period. Other than treatment stages, change of employment status due to the illness was the most significant predictor of illness-related uncertainty. The illness trajectory of PBT remains ambiguous, complex, and unpredictable, leading to a high incidence of uncertainty. There was variation in the subscales of uncertainty depending on treatment status. Although patients who are newly diagnosed reported the highest scores on most of the subscales, patients on treatment felt more uncertain about unpredictability of symptoms than other groups. Due to the complexity and impact of the disease, associated symptoms, and interference with functional status, comprehensive assessment of patients

  19. Cognitive dysfunction in children with brain tumors at diagnosis

    PubMed Central

    Studer, Martina; Ritter, Barbara Catherine; Steinlin, Maja; Leibundgut, Kurt; Heinks, Theda

    2015-01-01

    Background Survivors of brain tumors have a high risk for a wide range of cognitive problems. These dysfunctions are caused by the lesion itself and its surgical removal, as well as subsequent treatments (chemo‐ and/or radiation therapy). Multiple recent studies have indicated that children with brain tumors (BT) might already exhibit cognitive problems at diagnosis, i.e., before the start of any medical treatment. The aim of the present study was to investigate the baseline neuropsychological profile in children with BT compared to children with an oncological diagnosis not involving the central nervous system (CNS). Methods Twenty children with BT and 27 children with an oncological disease without involvement of the CNS (age range: 6.1–16.9 years) were evaluated with an extensive battery of neuropsychological tests tailored to the patient's age. Furthermore, the child and his/her parent(s) completed self‐report questionnaires about emotional functioning and quality of life. In both groups, tests were administered before any therapeutic intervention such as surgery, chemotherapy, or irradiation. Groups were comparable with regard to age, gender, and socioeconomic status. Results Compared to the control group, patients with BTs performed significantly worse in tests of working memory, verbal memory, and attention (effect sizes between 0.28 and 0.47). In contrast, the areas of perceptual reasoning, processing speed, and verbal comprehension were preserved at the time of measurement. Conclusion Our results highlight the need for cognitive interventions early in the treatment process in order to minimize or prevent academic difficulties as patients return to school. Pediatr Blood Cancer 2015;62:1805–1812. © 2015 The Authors. Pediatric Blood & Cancer, published by Wiley Periodicals, Inc. PMID:26053691

  20. Household pesticides and risk of pediatric brain tumors.

    PubMed Central

    Pogoda, J M; Preston-Martin, S

    1997-01-01

    A follow-up to a population-based case-control study of pediatric brain tumors in Los Angeles County, California, involving mothers of 224 cases and 218 controls, investigated the risk of household pesticide use from pregnancy to diagnosis. Risk was significantly elevated for prenatal exposure to flea/tick pesticides -odds ratio (OR) = 1.7; 95% confidence interval (CI), 1.1-2.6-, particularly among subjects less than 5 years old at diagnosis (OR = 2.5; CI, 1. 2-5.5). Prenatal risk was highest for mothers who prepared, applied, or cleaned up flea/tick products themselves (OR = 2.2; CI, 1.1-4.2; for subjects <5 years of age, OR = 5.4; CI, 1.3-22.3). A significant trend of increased risk with increased exposure was observed for number of pets treated (p = 0.04). Multivariate analysis of types of flea/tick products indicated that sprays/foggers were the only products significantly related to risk (OR =10.8; CI, 1.3-89.1). Elevated risks were not observed for termite or lice treatments, pesticides for nuisance pests, or yard and garden insecticides, herbicides, fungicides, or snail killer. Certain precautions,if ignored, were associated with significant increased risk: evacuating the house after spraying or dusting for pests (OR = 1.6; CI, 1.0-2.6), delaying the harvest of food after pesticide treatment (OR = 3.6; CI, 1.0-13.7), and following instructions on pesticide labels (OR = 3. 7;CI, 1.5-9.6). These findings indicate that chemicals used in flea/tick products may increase risk of pediatric brain tumors and suggest that further research be done to pinpoint specific chemicals involved. PMID:9370522

  1. Drug-Resistant Brain Metastases: A Role for Pharmacology, Tumor Evolution, and Too-Late Therapy.

    PubMed

    Stricker, Thomas; Arteaga, Carlos L

    2015-11-01

    Two recent studies report deep molecular profiling of matched brain metastases and primary tumors. In both studies, somatic alterations in the brain metastases were frequently discordant with those in the primary tumor, suggesting divergent evolution at metastatic sites and raising questions about the use of biomarkers in patients in clinical trials with targeted therapies.

  2. Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

    NASA Astrophysics Data System (ADS)

    Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

    2012-01-01

    The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

  3. Knowledge-based deformable surface model with application to segmentation of brain structures in MRI

    NASA Astrophysics Data System (ADS)

    Ghanei, Amir; Soltanian-Zadeh, Hamid; Elisevich, Kost; Fessler, Jeffrey A.

    2001-07-01

    We have developed a knowledge-based deformable surface for segmentation of medical images. This work has been done in the context of segmentation of hippocampus from brain MRI, due to its challenge and clinical importance. The model has a polyhedral discrete structure and is initialized automatically by analyzing brain MRI sliced by slice, and finding few landmark features at each slice using an expert system. The expert system decides on the presence of the hippocampus and its general location in each slice. The landmarks found are connected together by a triangulation method, to generate a closed initial surface. The surface deforms under defined internal and external force terms thereafter, to generate an accurate and reproducible boundary for the hippocampus. The anterior and posterior (AP) limits of the hippocampus is estimated by automatic analysis of the location of brain stem, and some of the features extracted in the initialization process. These data are combined together with a priori knowledge using Bayes method to estimate a probability density function (pdf) for the length of the structure in sagittal direction. The hippocampus AP limits are found by optimizing this pdf. The model is tested on real clinical data and the results show very good model performance.

  4. Collective behavior of brain tumor cells: The role of hypoxia

    NASA Astrophysics Data System (ADS)

    Khain, Evgeniy; Katakowski, Mark; Hopkins, Scott; Szalad, Alexandra; Zheng, Xuguang; Jiang, Feng; Chopp, Michael

    2011-03-01

    We consider emergent collective behavior of a multicellular biological system. Specifically, we investigate the role of hypoxia (lack of oxygen) in migration of brain tumor cells. We performed two series of cell migration experiments. In the first set of experiments, cell migration away from a tumor spheroid was investigated. The second set of experiments was performed in a typical wound-healing geometry: Cells were placed on a substrate, a scratch was made, and cell migration into the gap was investigated. Experiments show a surprising result: Cells under normal and hypoxic conditions have migrated the same distance in the “spheroid” experiment, while in the “scratch” experiment cells under normal conditions migrated much faster than under hypoxic conditions. To explain this paradox, we formulate a discrete stochastic model for cell dynamics. The theoretical model explains our experimental observations and suggests that hypoxia decreases both the motility of cells and the strength of cell-cell adhesion. The theoretical predictions were further verified in independent experiments.

  5. Donepezil in Treating Young Patients With Primary Brain Tumors Previously Treated With Radiation Therapy to the Brain

    ClinicalTrials.gov

    2016-07-26

    Brain and Central Nervous System Tumors; Cognitive/Functional Effects; Long-term Effects Secondary to Cancer Therapy in Children; Neurotoxicity; Psychosocial Effects of Cancer and Its Treatment; Radiation Toxicity

  6. A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

    PubMed

    Nanthagopal, A Padma; Rajamony, R Sukanesh

    2012-07-01

    The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method. PMID:22621242

  7. Occupational exposure to electromagnetic fields and the occurrence of brain tumors. An analysis of possible associations

    SciTech Connect

    Lin, R.S.; Dischinger, P.C.; Conde, J.; Farrell, K.P.

    1985-06-01

    To explore the association between occupation and the occurrence of brain tumor, an epidemiologic study was conducted using data from the death certificates of 951 adult white male Maryland residents who died of brain tumor during the period 1969 through 1982. Compared with the controls, men employed in electricity-related occupations, such as electrician, electric or electronic engineer, and utility company serviceman, were found to experience a significantly higher proportion of primary brain tumors. An increase in the odds ratio for brain tumor was found to be positively related to electromagnetic (EM) field exposure levels. Furthermore, the mean age at death was found to be significantly younger among cases in the presumed high EM-exposure group. These findings suggest that EM exposure may be associated with the pathogenesis of brain tumors, particularly in the promoting stage.

  8. Calcium Channels and Associated Receptors in Malignant Brain Tumor Therapy.

    PubMed

    Morrone, Fernanda B; Gehring, Marina P; Nicoletti, Natália F

    2016-09-01

    Malignant brain tumors are highly lethal and aggressive. Despite recent advances in the current therapies, which include the combination of surgery and radio/chemotherapy, the average survival rate remains poor. Altered regulation of ion channels is part of the neoplastic transformation, which suggests that ion channels are involved in cancer. Distinct classes of calcium-permeable channels are abnormally expressed in cancer and are likely involved in the alterations underlying malignant growth. Specifically, cytosolic Ca(2+) activity plays an important role in the regulation of cell proliferation, and Ca(2+) signaling is altered in proliferating tumor cells. A series of previous studies emphasized the importance of the T-type low-voltage-gated calcium channels (VGCC) in different cancer types, including gliomas, and remarkably, pharmacologic inhibition of T-type VGCC caused antiproliferative effects and triggered apoptosis of human glioma cells. Other calcium permeable channels, such as transient receptor potential (TRP) channels, contribute to changes in Ca(2+) by modulating the driving force for Ca(2+) entry, and some TRP channels are required for proliferation and migration in gliomas. Furthermore, recent evidence shows that TRP channels contribute to the progression and survival of the glioblastoma patients. Likewise, the purinergic P2X7 receptor acts as a direct conduit for Ca(2+)-influx and an indirect activator of voltage-gated Ca(2+)-channel. Evidence also shows that P2X7 receptor activation is linked to elevated expression of inflammation promoting factors, tumor cell migration, an increase in intracellular mobilization of Ca(2+), and membrane depolarization in gliomas. Therefore, this review summarizes the recent findings on calcium channels and associated receptors as potential targets to treat malignant gliomas. PMID:27418672

  9. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

    PubMed Central

    Yang, Zhang; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  10. A modified probabilistic neural network for partial volume segmentation in brain MR image.

    PubMed

    Song, Tao; Jamshidi, Mo M; Lee, Roland R; Huang, Mingxiong

    2007-09-01

    A modified probabilistic neural network (PNN) for brain tissue segmentation with magnetic resonance imaging (MRI) is proposed. In this approach, covariance matrices are used to replace the singular smoothing factor in the PNN's kernel function, and weighting factors are added in the pattern of summation layer. This weighted probabilistic neural network (WPNN) classifier can account for partial volume effects, which exist commonly in MRI, not only in the final result stage, but also in the modeling process. It adopts the self-organizing map (SOM) neural network to overly segment the input MR image, and yield reference vectors necessary for probabilistic density function (pdf) estimation. A supervised "soft" labeling mechanism based on Bayesian rule is developed, so that weighting factors can be generated along with corresponding SOM reference vectors. Tissue classification results from various algorithms are compared, and the effectiveness and robustness of the proposed approach are demonstrated. PMID:18220190

  11. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

    PubMed

    Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  12. Using 3-D shape models to guide segmentation of MR brain images.

    PubMed Central

    Hinshaw, K. P.; Brinkley, J. F.

    1997-01-01

    Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters. Images Figure 1 Figure 2 Figure 3 Figure 4 PMID:9357670

  13. Magnetic resonance spectroscopy for detection of choline kinase inhibition in the treatment of brain tumors

    PubMed Central

    Kumar, Manoj; Arlauckas, Sean P.; Saksena, Sona; Verma, Gaurav; Ittyerah, Ranjit; Pickup, Stephen; Popov, Anatoliy V.; Delikatny, Edward J.; Poptani, Harish

    2015-01-01

    Abnormal choline metabolism is a hallmark of cancer and is associated with oncogenesis and tumor progression. Increased choline is consistently observed in both pre-clinical tumor models and in human brain tumors by proton magnetic resonance spectroscopy (MRS). Thus, inhibition of choline metabolism using specific choline kinase inhibitors such as MN58b may be a promising new strategy for treatment of brain tumors. We demonstrate the efficacy of MN58b in suppressing phosphocholine production in three brain tumor cell lines. In vivo MRS studies of rats with intra-cranial F98-derived brain tumors showed a significant decrease in tumor total choline concentration after treatment with MN58b. High resolution MRS of tissue extracts confirmed that this decrease was due to a significant reduction in phosphocholine. Concomitantly, a significant increase in poly-unsaturated lipid resonances was also observed in treated tumors, indicating apoptotic cell death. Magnetic resonance imaging (MRI) based volume measurements demonstrated a significant growth arrest in the MN58b-treated tumors in comparison to saline-treated controls. Histologically, MN58b-treated tumors showed decreased cell density, as well as increased apoptotic cells. These results suggest that inhibition of choline kinase can be used as an adjuvant to chemotherapy in the treatment of brain tumors and that decreases in total choline observed by MRS can be used as an effective phamacodynamic biomarker of treatment response. PMID:25657334

  14. Integration of Sparse Multi-modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation

    PubMed Central

    Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

    2014-01-01

    Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination process. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8 months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6 months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter. PMID:24291615

  15. Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images.

    PubMed

    Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

    2014-12-01

    Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.

  16. Microtubule-associated protein tau in bovine retinal photoreceptor rod outer segments: comparison with brain tau

    PubMed Central

    Yamazaki, Akio; Nishizawa, Yuji; Matsuura, Isao; Hayashi, Fumio; Usukura, Jiro; Bondarenko, Vladimir A.

    2013-01-01

    Recent studies have suggested a possible involvement of abnormal tau in some retinal degenerative diseases. The common view in these studies is that these retinal diseases share the mechanism of tau-mediated degenerative diseases in brain and that information about these brain diseases may be directly applied to explain these retinal diseases. Here we collectively examine this view by revealing three basic characteristics of tau in the rod outer segment (ROS) of bovine retinal photoreceptors, i.e., its isoforms, its phosphorylation mode and its interaction with microtubules, and by comparing them with those of brain tau. We find that ROS contains at least four isoforms: three are identical to those in brain and one is unique in ROS. All ROS isoforms, like brain isoforms, are modified with multiple phosphate molecules; however, ROS isoforms show their own specific phosphorylation pattern, and these phosphorylation patterns appear not to be identical to those of brain tau. Interestingly, some ROS isoforms, under the normal conditions, are phosphorylated at the sites identical to those in Alzheimer’s patient isoforms. Surprisingly, a large portion of ROS isoforms tightly associates with a membranous component(s) other than microtubules, and this association is independent of their phosphorylation states. These observations strongly suggest that tau plays various roles in ROS and that some of these functions may not be comparable to those of brain tau. We believe that knowledge about tau in the entire retinal network and/or its individual cells are also essential for elucidation of tau-mediated retinal diseases, if any. PMID:23712071

  17. Segmentation and visualization of brain lesions in multispectral magnetic resonance images.

    PubMed

    Holden, M; Steen, E; Lundervold, A

    1995-01-01

    In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination of a recently proposed contour detection algorithm and Haslett's contextual classification method extended to 3D. This classification method is used in the classification step of a rendering model suggested by Drebin et al. for visualizing normal and pathological tissue structures in the brain. We evaluate the combination of these two methodologies, and identify some problems which have to be solved in order to develop a clinical useful tool. PMID:7780944

  18. Statistical model of laminar structure for atlas-based segmentation of the fetal brain from in utero MR images

    NASA Astrophysics Data System (ADS)

    Habas, Piotr A.; Kim, Kio; Chandramohan, Dharshan; Rousseau, Francois; Glenn, Orit A.; Studholme, Colin

    2009-02-01

    Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain segmentation are limited in their ability to accurately delineate complex structures of developing tissues from fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient (DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to 0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.

  19. Skeleton-based region competition for automated gray matter and white matter segmentation of human brain MR images

    NASA Astrophysics Data System (ADS)

    Chu, Yong; Chen, Ya-Fang; Su, Min-Ying; Nalcioglu, Orhan

    2005-04-01

    Image segmentation is an essential process for quantitative analysis. Segmentation of brain tissues in magnetic resonance (MR) images is very important for understanding the structural-functional relationship for various pathological conditions, such as dementia vs. normal brain aging. Different brain regions are responsible for certain functions and may have specific implication for diagnosis. Segmentation may facilitate the analysis of different brain regions to aid in early diagnosis. Region competition has been recently proposed as an effective method for image segmentation by minimizing a generalized Bayes/MDL criterion. However, it is sensitive to initial conditions - the "seeds", therefore an optimal choice of "seeds" is necessary for accurate segmentation. In this paper, we present a new skeleton-based region competition algorithm for automated gray and white matter segmentation. Skeletons can be considered as good "seed regions" since they provide the morphological a priori information, thus guarantee a correct initial condition. Intensity gradient information is also added to the global energy function to achieve a precise boundary localization. This algorithm was applied to perform gray and white matter segmentation using simulated MRI images from a realistic digital brain phantom. Nine different brain regions were manually outlined for evaluation of the performance in these separate regions. The results were compared to the gold-standard measure to calculate the true positive and true negative percentages. In general, this method worked well with a 96% accuracy, although the performance varied in different regions. We conclude that the skeleton-based region competition is an effective method for gray and white matter segmentation.

  20. Biphasic modeling of brain tumor biomechanics and response to radiation treatment.

    PubMed

    Angeli, Stelios; Stylianopoulos, Triantafyllos

    2016-06-14

    Biomechanical forces are central in tumor progression and response to treatment. This becomes more important in brain cancers where tumors are surrounded by tissues with different mechanical properties. Existing mathematical models ignore direct mechanical interactions of the tumor with the normal brain. Here, we developed a clinically relevant model, which predicts tumor growth accounting directly for mechanical interactions. A three-dimensional model of the gray and white matter and the cerebrospinal fluid was constructed from magnetic resonance images of a normal brain. Subsequently, a biphasic tissue growth theory for an initial tumor seed was employed, incorporating the effects of radiotherapy. Additionally, three different sets of brain tissue properties taken from the literature were used to investigate their effect on tumor growth. Results show the evolution of solid stress and interstitial fluid pressure within the tumor and the normal brain. Heterogeneous distribution of the solid stress exerted on the tumor resulted in a 35% spatial variation in cancer cell proliferation. Interestingly, the model predicted that distant from the tumor, normal tissues still undergo significant deformations while it was found that intratumoral fluid pressure is elevated. Our predictions relate to clinical symptoms of brain cancers and present useful tools for therapy planning. PMID:27086116

  1. A Unified Framework for Cross-modality Multi-atlas Segmentation of Brain MRI

    PubMed Central

    Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van

    2013-01-01

    Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single final segmentation. Such label fusion schemes commonly rely on the similarity between intensity values of the atlases and target scan, which is often problematic in medical imaging - in particular, when the atlases and target images are obtained via different sensor types or imaging protocols. In this paper, we present a generative probabilistic model that yields an algorithm for solving the atlas-to-target registrations and label fusion steps simultaneously. The proposed model does not directly rely on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing interdependence between the registrations. We use variational expectation maximization and the Demons registration framework in order to efficiently identify the most probable segmentation and registrations. We use two sets of experiments to illustrate the approach, where proton density (PD) MRI atlases are used to segment T1-weighted brain scans and vice versa. Our results clearly demonstrate the accuracy gain due to exploiting within-target intensity consistency and integrating registration into label fusion. PMID:24001931

  2. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data.

    PubMed

    Niedworok, Christian J; Brown, Alexander P Y; Jorge Cardoso, M; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W

    2016-01-01

    The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

  3. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data

    PubMed Central

    Niedworok, Christian J.; Brown, Alexander P. Y.; Jorge Cardoso, M.; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W.

    2016-01-01

    The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

  4. Automated ensemble segmentation of epithelial proliferation, necrosis, and fibrosis using scatter tumor imaging

    NASA Astrophysics Data System (ADS)

    Garcia-Allende, P. Beatriz; Conde, Olga M.; Krishnaswamy, Venkataramanan; Hoopes, P. Jack; Pogue, Brian W.; Mirapeix, Jesus; Lopez-Higuera, Jose M.

    2010-04-01

    Conventional imaging systems used today in surgical settings rely on contrast enhancement based on color and intensity and they are not sensitive to morphology changes at the microscopic level. Elastic light scattering spectroscopy has been shown to distinguish ultra-structural changes in tissue. Therefore, it could provide this intrinsic contrast being enormously useful in guiding complex surgical interventions. Scatter parameters associated with epithelial proliferation, necrosis and fibrosis in pancreatic tumors were previously estimated in a quantitative manner. Subtle variations were encountered across the distinct diagnostic categories. This work proposes an automated methodology to correlate these variations with their corresponding tumor morphologies. A new approach based on the aggregation of the predictions of K-nearest neighbors (kNN) algorithm and Artificial Neural Networks (ANNs) has been developed. The major benefit obtained from the combination of the distinct classifiers is a significant increase in the number of pixel localizations whose corresponding tissue type is reliably assured. Pseudo-color diagnosis images are provided showing a strong correlation with sample segmentations performed by a veterinary pathologist.

  5. Vorinostat and Temozolomide in Treating Young Patients With Relapsed or Refractory Primary Brain Tumors or Spinal Cord Tumors

    ClinicalTrials.gov

    2013-05-01

    Childhood Atypical Teratoid/Rhabdoid Tumor; Childhood Central Nervous System Choriocarcinoma; Childhood Central Nervous System Embryonal Tumor; Childhood Central Nervous System Germinoma; Childhood Central Nervous System Mixed Germ Cell Tumor; Childhood Central Nervous System Teratoma; Childhood Central Nervous System Yolk Sac Tumor; Childhood Choroid Plexus Tumor; Childhood Craniopharyngioma; Childhood Ependymoblastoma; Childhood Grade I Meningioma; Childhood Grade II Meningioma; Childhood Grade III Meningioma; Childhood High-grade Cerebellar Astrocytoma; Childhood High-grade Cerebral Astrocytoma; Childhood Infratentorial Ependymoma; Childhood Low-grade Cerebellar Astrocytoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Medulloepithelioma; Childhood Mixed Glioma; Childhood Oligodendroglioma; Childhood Supratentorial Ependymoma; Extra-adrenal Paraganglioma; Recurrent Childhood Brain Stem Glioma; Recurrent Childhood Central Nervous System Embryonal Tumor; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Pineoblastoma; Recurrent Childhood Spinal Cord Neoplasm; Recurrent Childhood Subependymal Giant Cell Astrocytoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Childhood Visual Pathway and Hypothalamic Glioma

  6. Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

    NASA Astrophysics Data System (ADS)

    Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

    2016-03-01

    The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

  7. Invited review--neuroimaging response assessment criteria for brain tumors in veterinary patients.

    PubMed

    Rossmeisl, John H; Garcia, Paulo A; Daniel, Gregory B; Bourland, John Daniel; Debinski, Waldemar; Dervisis, Nikolaos; Klahn, Shawna

    2014-01-01

    The evaluation of therapeutic response using cross-sectional imaging techniques, particularly gadolinium-enhanced MRI, is an integral part of the clinical management of brain tumors in veterinary patients. Spontaneous canine brain tumors are increasingly recognized and utilized as a translational model for the study of human brain tumors. However, no standardized neuroimaging response assessment criteria have been formulated for use in veterinary clinical trials. Previous studies have found that the pathophysiologic features inherent to brain tumors and the surrounding brain complicate the use of the response evaluation criteria in solid tumors (RECIST) assessment system. Objectives of this review are to describe strengths and limitations of published imaging-based brain tumor response criteria and propose a system for use in veterinary patients. The widely used human Macdonald and response assessment in neuro-oncology (RANO) criteria are reviewed and described as to how they can be applied to veterinary brain tumors. Discussion points will include current challenges associated with the interpretation of brain tumor therapeutic responses such as imaging pseudophenomena and treatment-induced necrosis, and how advancements in perfusion imaging, positron emission tomography, and magnetic resonance spectroscopy have shown promise in differentiating tumor progression from therapy-induced changes. Finally, although objective endpoints such as MR imaging and survival estimates will likely continue to comprise the foundations for outcome measures in veterinary brain tumor clinical trials, we propose that in order to provide a more relevant therapeutic response metric for veterinary patients, composite response systems should be formulated and validated that combine imaging and clinical assessment criteria. PMID:24219161

  8. INVITED REVIEW – NEUROIMAGING RESPONSE ASSESSMENT CRITERIA FOR BRAIN TUMORS IN VETERINARY PATIENTS

    PubMed Central

    Rossmeisl, John H.; Garcia, Paulo A.; Daniel, Gregory B.; Bourland, John Daniel; Debinski, Waldemar; Dervisis, Nikolaos; Klahn, Shawna

    2013-01-01

    The evaluation of therapeutic response using cross-sectional imaging techniques, particularly gadolinium-enhanced MRI, is an integral part of the clinical management of brain tumors in veterinary patients. Spontaneous canine brain tumors are increasingly recognized and utilized as a translational model for the study of human brain tumors. However, no standardized neuroimaging response assessment criteria have been formulated for use in veterinary clinical trials. Previous studies have found that the pathophysiologic features inherent to brain tumors and the surrounding brain complicate the use of the Response Evaluation Criteria in Solid Tumors (RECIST) assessment system. Objectives of this review are to describe strengths and limitations of published imaging-based brain tumor response criteria and propose a system for use in veterinary patients. The widely used human Macdonald and Response Assessment in Neuro-oncology (RANO) criteria are reviewed and described as to how they can be applied to veterinary brain tumors. Discussion points will include current challenges associated with the interpretation of brain tumor therapeutic responses such as imaging pseudophenomena and treatment-induced necrosis, and how advancements in perfusion imaging, positron emission tomography, and magnetic resonance spectroscopy have shown promise in differentiating tumor progression from therapy-induced changes. Finally, although objective endpoints such as MR-imaging and survival estimates will likely continue to comprise the foundations for outcome measures in veterinary brain tumor clinical trials, we propose that in order to provide a more relevant therapeutic response metric for veterinary patients, composite response systems should be formulated and validated that combine imaging and clinical assessment criteria. PMID:24219161

  9. A multicenter study of primary brain tumor incidence in Australia (2000–2008)

    PubMed Central

    Dobes, Martin; Shadbolt, Bruce; Khurana, Vini G.; Jain, Sanjiv; Smith, Sarah F.; Smee, Robert; Dexter, Mark; Cook, Raymond

    2011-01-01

    There are conflicting reports from Europe and North America regarding trends in the incidence of primary brain tumor, whereas the incidence of primary brain tumors in Australia is currently unknown. We aimed to determine the incidence in Australia with age-, sex-, and benign-versus-malignant histology-specific analyses. A multicenter study was performed in the state of New South Wales (NSW) and the Australian Capital Territory (ACT), which has a combined population of >7 million with >97% rate of population retention for medical care. We retrospectively mined pathology databases servicing neurosurgical centers in NSW and ACT for histologically confirmed primary brain tumors diagnosed from January 2000 through December 2008. Data were weighted for patient outflow and data completeness. Incidence rates were age standardized and trends analyzed using joinpoint analysis. A weighted total of 7651 primary brain tumors were analyzed. The overall US-standardized incidence of primary brain tumors was 11.3 cases 100 000 person-years (±0.13; 95% confidence interval, 9.8–12.3) during the study period with no significant linear increase. A significant increase in primary malignant brain tumors from 2000 to 2008 was observed; this appears to be largely due to an increase in malignant tumor incidence in the ≥65-year age group. This collection represents the most contemporary data on primary brain tumor incidence in Australia. Whether the observed increase in malignant primary brain tumors, particularly in persons aged ≥65 years, is due to improved detection, diagnosis, and care delivery or a true change in incidence remains undetermined. We recommend a direct, uniform, and centralized approach to monitoring primary brain tumor incidence that can be independent of multiple interstate cancer registries. PMID:21727214

  10. Penetration of intra-arterially administered vincristine in experimental brain tumor1,2

    PubMed Central

    Boyle, Frances M.; Eller, Susan L.; Grossman, Stuart A.

    2004-01-01

    Vincristine is an integral part of the “PCV” regimen that is commonly administered to treat primary brain tumors. The efficacy of vincristine as a single agent in these tumors has been poorly studied. This study was designed to determine whether vincristine enters normal rat brain or an intracranially or subcutaneously implanted glioma and to assess the presence of the efflux pump P-glycoprotein (P-gp) on tumor and vascular endothelial cells. The 9L rat gliosarcoma was implanted intracranially and subcutaneously in three Fischer 344 rats. On day 7, [3H]vincristine (50 μCi, 4.8 μg) was injected into the carotid artery, and the animals were euthanized 10 or 20 min later. Quantitative autoradiography revealed that vincristine levels in the liver were 6- to 11-fold greater than in the i.c. tumor, and 15- to 37-fold greater than in normal brain, the reverse of the expected pattern with intra-arterial delivery. Vincristine levels in the s.c. tumor were 2-fold higher than levels in the i.c. tumor. P-gp was detected with JSB1 antibody in vascular endothelium of both normal brain and the i.c. tumor, but not in the tumor cells in either location, or in endothelial cells in the s.c. tumor. These results demonstrate that vincristine has negligible penetration of normal rat brain or i.c. 9L glioma despite intra-arterial delivery and the presence of blood-brain barrier dysfunction as demonstrated by Evan’s blue. Furthermore, this study suggests that P-gp-mediated efflux from endothelium may explain these findings. The lack of penetration of vincristine into brain tumor and the paucity of single-agent activity studies suggest that vincristine should not be used in the treatment of primary brain tumors. PMID:15494097

  11. Regional blood-to-tissue transport in RT-9 brain tumors.

    PubMed

    Molnar, P; Blasberg, R G; Horowitz, M; Smith, B; Fenstermacher, J

    1983-06-01

    Regional blood-to-tissue transport, expressed as a unidirectional transfer rate constant (K), was measured in experimental RT-9 brain tumors using 14C-alpha-aminoisobutyric acid (AIB) and quantitative autoradiographic techniques. The magnitude of K depends on the permeability, surface area, and blood flow of the tissue capillaries. The transfer rate constant was variable within tumor tissue (range 0.001 to 0.178 ml/gm/min) and depended on tumor size, location (intraparenchymal, meningeal, or choroid plexus associated), and to a lesser extent on necrosis and cyst formation. Brain adjacent to tumor had higher K values, particularly around larger tumors (0.004 to 0.014 ml/gm/min), than corresponding brain regions in the contralateral hemisphere (0.001 to 0.002 ml/gm/min). Estimates of the fractional extraction of AIB by intraparenchymal tumors were between 0.008 and 0.4 ml/gm/min. Values of fractional extraction in this range indicate that tumor capillaries are not freely permeable to this solute. The values of K measured with AIB in this study, for the most part, approximate the permeability-surface area product of tumor and brain capillaries. The experimental data suggest that the permeability-surface area characteristics of the microvasculature in small RT-9 tumors are similar to those of the host tissue, whereas the microvasculature of larger RT-9 tumors is influenced more by intrinsic tumor factors.

  12. 18F-FDG PET and MR Imaging Associations Across a Spectrum of Pediatric Brain Tumors: A Report from the Pediatric Brain Tumor Consortium

    PubMed Central

    Zukotynski, Katherine; Fahey, Frederic; Kocak, Mehmet; Kun, Larry; Boyett, James; Fouladi, Maryam; Vajapeyam, Sridhar; Treves, Ted; Poussaint, Tina Y.

    2014-01-01

    The purpose of this study was to describe 18F-FDG uptake across a spectrum of pediatric brain tumors and correlate 18F-FDG PET with MR imaging variables, progression-free survival (PFS), and overall survival (OS). Methods A retrospective analysis was conducted of children enrolled in phase I/II clinical trials through the Pediatric Brain Tumor Consortium from August 2000 to June 2010. PET variables were summarized within diagnostic categories using descriptive statistics. Associations of PET with MR imaging variables and PFS and OS by tumor types were evaluated. Results Baseline 18F-FDG PET was available in 203 children; 66 had newly diagnosed brain tumors, and 137 had recurrent/refractory brain tumors before enrolling in a Pediatric Brain Tumor Consortium trial. MR imaging was performed within 2 wk of PET and before therapy in all cases. The 18F-FDG uptake pattern and MR imaging contrast enhancement (CE) varied by tumor type. On average, glioblastoma multiforme and medulloblastoma had uniform, intense uptake throughout the tumor, whereas brain stem gliomas (BSGs) had low uptake in less than 50% of the tumor and ependymoma had low uptake throughout the tumor. For newly diagnosed BSG, correlation of 18F-FDG uptake with CE portended reduced OS (P = 0.032); in refractory/recurrent BSG, lack of correlation between 18F-FDG uptake and CE suggested decreased PFS (P = 0.023). In newly diagnosed BSG for which more than 50% of the tumor had 18F-FDG uptake, there was a suggestion of lower apparent diffusion coefficient (P = 0.061) and decreased PFS (P = 0.065). Conclusion 18F-FDG PET and MR imaging showed a spectrum of patterns depending on tumor type. In newly diagnosed BSG, the correlation of 18F-FDG uptake and CE suggested decreased OS, likely related to more aggressive disease. When more than 50% of the tumor had 18F-FDG uptake, the apparent diffusion coefficient was lower, consistent with increased cellularity. In refractory/recurrent BSG, poor correlation between 18F

  13. SU-C-9A-03: Simultaneous Deconvolution and Segmentation for PET Tumor Delineation Using a Variational Method

    SciTech Connect

    Li, L; Tan, S; Lu, W; D'Souza, W

    2014-06-01

    Purpose: To implement a new method that integrates deconvolution with segmentation under the variational framework for PET tumor delineation. Methods: Deconvolution and segmentation are both challenging problems in image processing. The partial volume effect (PVE) makes tumor boundaries in PET image blurred which affects the accuracy of tumor segmentation. Deconvolution aims to obtain a PVE-free image, which can help to improve the segmentation accuracy. Conversely, a correct localization of the object boundaries is helpful to estimate the blur kernel, and thus assist in the deconvolution. In this study, we proposed to solve the two problems simultaneously using a variational method so that they can benefit each other. The energy functional consists of a fidelity term and a regularization term, and the blur kernel was limited to be the isotropic Gaussian kernel. We minimized the energy functional by solving the associated Euler-Lagrange equations and taking the derivative with respect to the parameters of the kernel function. An alternate minimization method was used to iterate between segmentation, deconvolution and blur-kernel recovery. The performance of the proposed method was tested on clinic PET images of patients with non-Hodgkin's lymphoma, and compared with seven other segmentation methods using the dice similarity index (DSI) and volume error (VE). Results: Among all segmentation methods, the proposed one (DSI=0.81, VE=0.05) has the highest accuracy, followed by the active contours without edges (DSI=0.81, VE=0.25), while other methods including the Graph Cut and the Mumford-Shah (MS) method have lower accuracy. A visual inspection shows that the proposed method localizes the real tumor contour very well. Conclusion: The result showed that deconvolution and segmentation can contribute to each other. The proposed variational method solve the two problems simultaneously, and leads to a high performance for tumor segmentation in PET. This work was supported

  14. Hierarchical brain tissue segmentation and its application in multiple sclerosis and Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Lei, Tianhu; Udupa, Jayaram K.; Moonis, Gul; Schwartz, Eric; Balcer, Laura

    2005-04-01

    Based on Fuzzy Connectedness (FC) object delineation principles and algorithms, a hierarchical brain tissue segmentation technique has been developed for MR images. After MR image background intensity inhomogeneity correction and intensity standardization, three FC objects for cerebrospinal fl