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Sample records for brain tumor segmentation

  1. Brain tumor segmentation with Deep Neural Networks.

    PubMed

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster.

  2. 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.

  3. Efficient multilevel brain tumor segmentation with integrated bayesian model classification.

    PubMed

    Corso, J J; Sharon, E; Dube, S; El-Saden, S; Sinha, U; Yuille, A

    2008-05-01

    We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.

  4. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

    PubMed

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-05-01

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

  5. Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images.

    PubMed

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-03-04

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 33 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0:88, 0:83, 0:77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0:78, 0:65, and 0:75 for the complete, core, and enhancing regions, respectively.

  6. Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation.

    PubMed

    Corso, Jason J; Sharon, Eitan; Yuille, Alan

    2006-01-01

    We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor.

  7. 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

  8. 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.

  9. 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.

  10. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

    NASA Astrophysics Data System (ADS)

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-01

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  11. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications.

    PubMed

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-21

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  12. Monitoring brain tumor response to therapy using MRI segmentation.

    PubMed

    Vaidyanathan, M; Clarke, L P; Hall, L O; Heidtman, C; Velthuizen, R; Gosche, K; Phuphanich, S; Wagner, H; Greenberg, H; Silbiger, M L

    1997-01-01

    The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.

  13. Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

    NASA Astrophysics Data System (ADS)

    Hemanth, D. Jude; Vijila, C. Kezi Selva; Anitha, J.

    2010-02-01

    Image segmentation is one of the significant digital image processing techniques commonly used in the medical field. One of the specific applications is tumor detection in abnormal Magnetic Resonance (MR) brain images. Fuzzy approaches are widely preferred for tumor segmentation which generally yields superior results in terms of accuracy. But most of the fuzzy algorithms suffer from the drawback of slow convergence rate which makes the system practically non-feasible. In this work, the application of modified Fuzzy C-means (FCM) algorithm to tackle the convergence problem is explored in the context of brain image segmentation. This modified FCM algorithm employs the concept of quantization to improve the convergence rate besides yielding excellent segmentation efficiency. This algorithm is experimented on real time abnormal MR brain images collected from the radiologists. A comprehensive feature vector is extracted from these images and used for the segmentation technique. An extensive feature selection process is performed which reduces the convergence time period and improve the segmentation efficiency. After segmentation, the tumor portion is extracted from the segmented image. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Thus, this work highlights the application of the modified algorithm for brain tumor detection in abnormal MR brain images.

  14. 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.

  15. MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes

    PubMed Central

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-01-01

    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 different 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 diffusion 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. PMID:25302005

  16. 3D variational brain tumor segmentation on a clustered feature set

    NASA Astrophysics Data System (ADS)

    Popuri, Karteek; Cobzas, Dana; Jagersand, Martin; Shah, Sirish L.; Murtha, Albert

    2009-02-01

    Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. Our work addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

  17. 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.

  18. Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique

    PubMed Central

    Jones, Timothy L.; Byrnes, Tiernan J.; Yang, Guang; Howe, Franklyn A.; Bell, B. Anthony; Barrick, Thomas R.

    2015-01-01

    Background There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. Methods DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Results Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. Conclusions D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning. PMID:25121771

  19. 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.

  20. 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.

  1. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

    PubMed

    Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

    2016-01-01

    Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

  2. 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.

  3. 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

  4. 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.

  5. 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 ...

  6. 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

  7. 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.

  8. Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning.

    PubMed

    Beyer, Gloria P; Velthuizen, Robert P; Murtagh, F Reed; Pearlman, James L

    2006-11-01

    The purpose of this study was to design the steps necessary to create a tumor volume outline from the results of two automated multispectral magnetic resonance imaging segmentation methods and integrate these contours into radiation therapy treatment planning. Algorithms were developed to create a closed, smooth contour that encompassed the tumor pixels resulting from two automated segmentation methods: k-nearest neighbors and knowledge guided. These included an automatic three-dimensional (3D) expansion of the results to compensate for their undersegmentation and match the extended contouring technique used in practice by radiation oncologists. Each resulting radiation treatment plan generated from the automated segmentation and from the outlining by two radiation oncologists for 11 brain tumor patients was compared against the volume and treatment plan from an expert radiation oncologist who served as the control. As part of this analysis, a quantitative and qualitative evaluation mechanism was developed to aid in this comparison. It was found that the expert physician reference volume was irradiated within the same level of conformity when using the plans generated from the contours of the segmentation methods. In addition, any uncertainty in the identification of the actual gross tumor volume by the segmentation methods, as identified by previous research into this area, had small effects when used to generate 3D radiation therapy treatment planning due to the averaging process in the generation of margins used in defining a planning target volume.

  9. 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.

  10. Brain tumor - primary - adults

    MedlinePlus

    ... Vestibular schwannoma (acoustic neuroma) - adults; Meningioma - adults; Cancer - brain tumor (adults) ... Primary brain tumors include any tumor that starts in the brain. Primary brain tumors can start from brain cells, ...

  11. 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 ...

  12. Brain tumor - children

    MedlinePlus

    ... children; Neuroglioma - children; Oligodendroglioma - children; Meningioma - children; Cancer - brain tumor (children) ... The cause of primary brain tumors is unknown. Primary brain tumors may ... (spread to nearby areas) Cancerous (malignant) Brain tumors ...

  13. 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 ...

  14. 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.

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

    PubMed Central

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

    2014-01-01

    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. PMID:24784396

  16. Brain tumor segmentation in 3D MRIs using an improved Markov random field model

    NASA Astrophysics Data System (ADS)

    Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

    2011-10-01

    Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.

  17. Brain Tumors (For Parents)

    MedlinePlus

    ... Old Feeding Your 1- to 2-Year-Old 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 ...

  18. Brain Tumors (For Parents)

    MedlinePlus

    ... Old Feeding Your 1- to 2-Year-Old Brain Tumors KidsHealth > For Parents > Brain Tumors A A ... radiation therapy or chemotherapy, or both. Types of Brain Tumors There are many different types of brain ...

  19. Pediatric Brain Tumor Foundation

    MedlinePlus

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

  20. Tumor Types: Understanding Brain Tumors

    MedlinePlus

    ... Resources Tools & Publications Tumor Types: Understanding Brain Tumors World Health Organization (WHO) Updates Official Classification of Tumors ... Central Nervous System On May 9, 2016, the World Health Organization (WHO) published an official reclassification of ...

  1. MRI brain tumor segmentation based on improved fuzzy c-means method

    NASA Astrophysics Data System (ADS)

    Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

    2009-10-01

    This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

  2. 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 ...

  3. American Brain Tumor Association

    MedlinePlus

    ... Molecule Read More ABTA News April 6, 2017 Chicago-Based American Brain Tumor Association’s Breakthrough for Brain ... Association 8550 W. Bryn Mawr Ave. Ste 550 Chicago, IL 60631 © 2014 American Brain Tumor Association Phone: ...

  4. Epidemiology of Brain Tumors.

    PubMed

    McNeill, Katharine A

    2016-11-01

    Brain tumors are the commonest solid tumor in children, leading to significant cancer-related mortality. Several hereditary syndromes associated with brain tumors are nonfamilial. Ionizing radiation is a well-recognized risk factor for brain tumors. Several industrial exposures have been evaluated for a causal association with brain tumor formation but the results are inconclusive. A casual association between the common mutagens of tobacco, alcohol, or dietary factors has not yet been established. There is no clear evidence that the incidence of brain tumors has changed over time. This article presents the descriptive epidemiology of the commonest brain tumors of children and adults.

  5. 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, ...

  6. 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

  7. 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

  8. Immunology of brain tumors.

    PubMed

    Roth, Patrick; Eisele, Günter; Weller, Michael

    2012-01-01

    Brain tumors of different origin, but notably malignant gliomas, are characterized by their immunosuppressive properties which allow them to escape the host's immune surveillance. The activating immune cell ligands that are expressed by tumor cells, together with potentially immunogenic antigens, are overridden by numerous immune inhibitory signals, with TGF-3 as the master immunosuppressive molecule (Figure 4.1).The ongoing investigation of mechanisms of tumor-derived immunosuppression allows for an increasing understanding of brain tumor immunology. Targeting different mechanisms of tumor-derived immunosuppression, such as inhibition of TGF-[, may represent a promising strategy for future immunotherapeutic approaches.

  9. 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.

  10. Familiality in brain tumors

    PubMed Central

    Blumenthal, Deborah T.; Cannon-Albright, Lisa A.

    2008-01-01

    Background: Familiality in brain tumors is not definitively substantiated. Methods: We used the Utah Population Data Base (UPDB), a genealogy representing the Utah pioneers and their descendants, record-linked to statewide cancer records, to describe the familial nature of primary brain cancer. We examined the familial clustering of primary brain tumors, including subgroups defined by histologic type and age at diagnosis. The UPDB includes 1,401 primary brain tumor cases defined as astrocytoma or glioblastoma, all with at least three generations of genealogy data. We tested the hypothesis of excess relatedness of brain tumor cases using the Genealogical Index of Familiality method. We estimated relative risks for brain tumors in relatives using rates of brain tumors estimated internally. Results: Significant excess relatedness was observed for astrocytomas and glioblastomas considered as a group (n = 1,401), for astrocytomas considered separately (n = 744), but not for glioblastomas considered separately (n = 658). Significantly increased risks to first- and second-degree relatives for astrocytomas were identified for relatives of astrocytomas considered separately. Significantly increased risks to first-degree relatives, but not second degree, were observed for astrocytoma and glioblastoma cases considered together, and for glioblastoma cases considered separately. Conclusions: This study provides strong evidence for a familial contribution to primary brain cancer risk. There is evidence that this familial aspect includes not only shared environment, but also a heritable component. Extended high-risk brain tumor pedigrees identified in the UPDB may provide the opportunity to identify predisposition genes responsible for familial brain tumors. GLOSSARY GBM = glioblastoma; GIF = Genealogical Index of Familiality; HGG = high-grade gliomas; ICD-O = International Classification of Disease–Oncology; LGG = low-grade gliomas; RR = relative risks; SEER = Surveillance

  11. Metastatic brain tumor

    MedlinePlus

    ... them create an advance directive and power of attorney for health care. Support Groups You can ease ... surgery Brain tumor - children Breast cancer Increased intracranial pressure Lung cancer - small cell Melanoma Renal cell carcinoma ...

  12. Brain Tumor Statistics

    MedlinePlus

    ... About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Press Releases Headlines Newsletter ... About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials News Careers Brain Tumor Information ...

  13. Pediatric brain tumor cell lines.

    PubMed

    Xu, Jingying; Margol, Ashley; Asgharzadeh, Shahab; Erdreich-Epstein, Anat

    2015-02-01

    Pediatric brain tumors as a group, including medulloblastomas, gliomas, and atypical teratoid rhabdoid tumors (ATRT) are the most common solid tumors in children and the leading cause of death from childhood cancer. Brain tumor-derived cell lines are critical for studying the biology of pediatric brain tumors and can be useful for initial screening of new therapies. Use of appropriate brain tumor cell lines for experiments is important, as results may differ depending on tumor properties, and can thus affect the conclusions and applicability of the model. Despite reports in the literature of over 60 pediatric brain tumor cell lines, the majority of published papers utilize only a small number of these cell lines. Here we list the approximately 60 currently-published pediatric brain tumor cell lines and summarize some of their central features as a resource for scientists seeking pediatric brain tumor cell lines for their research.

  14. 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

  15. 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) ...

  16. 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

  17. Freehand 3D ultrasound breast tumor segmentation

    NASA Astrophysics Data System (ADS)

    Liu, Qi; Ge, Yinan; Ou, Yue; Cao, Biao

    2007-12-01

    It is very important for physicians to accurately determine breast tumor location, size and shape in ultrasound image. The precision of breast tumor volume quantification relies on the accurate segmentation of the images. Given the known location and orientation of the ultrasound probe, We propose using freehand three dimensional (3D) ultrasound to acquire original images of the breast tumor and the surrounding tissues in real-time, after preprocessing with anisotropic diffusion filtering, the segmentation operation is performed slice by slice based on the level set method in the image stack. For the segmentation on each slice, the user can adjust the parameters to fit the requirement in the specified image in order to get the satisfied result. By the quantification procedure, the user can know the tumor size varying in different images in the stack. Surface rendering and interpolation are used to reconstruct the 3D breast tumor image. And the breast volume is constructed by the segmented contours in the stack of images. After the segmentation, the volume of the breast tumor in the 3D image data can be obtained.

  18. Adolescent and Pediatric Brain Tumors

    MedlinePlus

    ... a child you love is diagnosed with a brain tumor, it is difficult to think about anything else. There are often more questions than answers. Your life can feel as though it has been turned upside ... Brain Tumor Association for information, insight and support. Our ...

  19. 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.

  20. Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

    NASA Astrophysics Data System (ADS)

    Velthuizen, Robert P.; Hall, Lawrence O.; Clarke, Laurence P.; Bensaid, Amine M.; Arrington, J. A.; Silbiger, Martin L.

    1993-07-01

    Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.

  1. 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

  2. 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

  3. Precision radiotherapy for brain tumors

    PubMed Central

    Yan, Ying; Guo, Zhanwen; Zhang, Haibo; Wang, Ning; Xu, Ying

    2012-01-01

    OBJECTIVE: Precision radiotherapy plays an important role in the management of brain tumors. This study aimed to identify global research trends in precision radiotherapy for brain tumors using a bibliometric analysis of the Web of Science. DATA RETRIEVAL: We performed a bibliometric analysis of data retrievals for precision radiotherapy for brain tumors containing the key words cerebral tumor, brain tumor, intensity-modulated radiotherapy, stereotactic body radiation therapy, stereotactic ablative radiotherapy, imaging-guided radiotherapy, dose-guided radiotherapy, stereotactic brachytherapy, and stereotactic radiotherapy using the Web of Science. SELECTION CRITERIA: Inclusion criteria: (a) peer-reviewed articles on precision radiotherapy for brain tumors which were published and indexed in the Web of Science; (b) type of articles: original research articles and reviews; (c) year of publication: 2002-2011. Exclusion criteria: (a) articles that required manual searching or telephone access; (b) Corrected papers or book chapters. MAIN OUTCOME MEASURES: (1) Annual publication output; (2) distribution according to country; (3) distribution according to institution; (4) top cited publications; (5) distribution according to journals; and (6) comparison of study results on precision radiotherapy for brain tumors. RESULTS: The stereotactic radiotherapy, intensity-modulated radiotherapy, and imaging-guided radiotherapy are three major methods of precision radiotherapy for brain tumors. There were 260 research articles addressing precision radiotherapy for brain tumors found within the Web of Science. The USA published the most papers on precision radiotherapy for brain tumors, followed by Germany and France. European Synchrotron Radiation Facility, German Cancer Research Center and Heidelberg University were the most prolific research institutes for publications on precision radiotherapy for brain tumors. Among the top 13 research institutes publishing in this field, seven

  4. Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images.

    DTIC Science & Technology

    1996-12-01

    RESONANCE BRAIN IMAGES L Introduction 1.1 Introduction Current technology enables the detection , diagnosis, and evaluation of many common and not so common...Thresholding is further used to segment the brain into regions. Edge detection methods are another approach to image segmentation. These meth- ods are...The FCM and AFCM displayed the best results for segmenting normal images while the FFCC provided better segmentation of tumorous regions. The FFCC

  5. 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.

  6. Brain tumor classification of microscopy images using deep residual learning

    NASA Astrophysics Data System (ADS)

    Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

    2016-12-01

    The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

  7. Spectroscopic-guided brain tumor resection

    NASA Astrophysics Data System (ADS)

    Lin, Wei-Chiang; Toms, Steven A.; Jansen, E. Duco; Mahadevan-Jansen, Anita

    2000-05-01

    A pilot in vivo study was conducted to investigate the feasibility of using optical spectroscopy for brain tumor margin detection. Fluorescence and diffuse reflectance spectra were acquired using a portable clinical spectroscopic system from normal brain tissues, tumors, and tumor margins in 21 brain tumor patients undergoing craniotomy. Results form this study show the potential of optical spectroscopy in detecting infiltrating tumor margins of primary brain tumors.

  8. Multi-parametric analysis and registration of brain tumors: constructing statistical atlases and diagnostic tools of predictive value.

    PubMed

    Davatzikos, Christos; Zacharaki, Evangelia I; Gooya, Ali; Clark, Vanessa

    2011-01-01

    We discuss computer-based image analysis algorithms of multi-parametric MRI of brain tumors, aiming to assist in early diagnosis of infiltrating brain tumors, and to construct statistical atlases summarizing population-based characteristics of brain tumors. These methods combine machine learning, deformable registration, multi-parametric segmentation, and biophysical modeling of brain tumors.

  9. Brain Tumor Symptoms

    MedlinePlus

    ... be associated with the type, size, and/or location of the tumor, as well as the treatments used to manage it. Surgery, radiation, chemotherapy, and other treatments all have the potential to ... American ...

  10. Glial brain tumor detection by using symmetry analysis

    NASA Astrophysics Data System (ADS)

    Pedoia, Valentina; Binaghi, Elisabetta; Balbi, Sergio; De Benedictis, Alessandro; Monti, Emanuele; Minotto, Renzo

    2012-02-01

    In this work a fully automatic algorithm to detect brain tumors by using symmetry analysis is proposed. In recent years a great effort of the research in field of medical imaging was focused on brain tumors segmentation. The quantitative analysis of MRI brain tumor allows to obtain useful key indicators of disease progression. The complex problem of segmenting tumor in MRI can be successfully addressed by considering modular and multi-step approaches mimicking the human visual inspection process. The tumor detection is often an essential preliminary phase to solvethe segmentation problem successfully. In visual analysis of the MRI, the first step of the experts cognitive process, is the detection of an anomaly respect the normal tissue, whatever its nature. An healthy brain has a strong sagittal symmetry, that is weakened by the presence of tumor. The comparison between the healthy and ill hemisphere, considering that tumors are generally not symmetrically placed in both hemispheres, was used to detect the anomaly. A clustering method based on energy minimization through Graph-Cut is applied on the volume computed as a difference between the left hemisphere and the right hemisphere mirrored across the symmetry plane. Differential analysis involves the loss the knowledge of the tumor side. Through an histogram analysis the ill hemisphere is recognized. Many experiments are performed to assess the performance of the detection strategy on MRI volumes in presence of tumors varied in terms of shapes positions and intensity levels. The experiments showed good results also in complex situations.

  11. 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.

  12. Unsupervised measurement of brain tumor volume on MR images.

    PubMed

    Velthuizen, R P; Clarke, L P; Phuphanich, S; Hall, L O; Bensaid, A M; Arrington, J A; Greenberg, H M; Silbiger, M L

    1995-01-01

    We examined unsupervised methods of segmentation of MR images of the brain for measuring tumor volume in response to treatment. Two clustering methods were used: fuzzy c-means and a nonfuzzy clustering algorithm. Results were compared with volume segmentations by two supervised methods, k-nearest neighbors and region growing, and all results were compared with manual labelings. Results of individual segmentations are presented as well as comparisons on the application of the different methods with 10 data sets of patients with brain tumors. Unsupervised segmentation is preferred for measuring tumor volumes in response to treatment, as it eliminates operator dependency and may be adequate for delineation of the target volume in radiation therapy. Some obstacles need to be overcome, in particular regarding the detection of anatomically relevant tissue classes. This study shows that these improvements are possible.

  13. 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.

  14. Segmentation of human brain using structural MRI.

    PubMed

    Helms, Gunther

    2016-04-01

    Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

  15. Parallel optimization of tumor model parameters for fast registration of brain tumor images

    NASA Astrophysics Data System (ADS)

    Zacharaki, Evangelia I.; Hogea, Cosmina S.; Shen, Dinggang; Biros, George; Davatzikos, Christos

    2008-03-01

    The motivation of this work is to register MR brain tumor images with a brain atlas. Such a registration method can make possible the pooling of data from different brain tumor patients into a common stereotaxic space, thereby enabling the construction of statistical brain tumor atlases. Moreover, it allows the mapping of neuroanatomical brain atlases into the patient's space, for segmenting brains and thus facilitating surgical or radiotherapy treatment planning. However, the methods developed for registration of normal brain images are not directly applicable to the registration of a normal atlas with a tumor-bearing image, due to substantial dissimilarity and lack of equivalent image content between the two images, as well as severe deformation or shift of anatomical structures around the tumor. Accordingly, a model that can simulate brain tissue death and deformation induced by the tumor is considered to facilitate the registration. Such tumor growth simulation models are usually initialized by placing a small seed in the normal atlas. The shape, size and location of the initial seed are critical for achieving topological equivalence between the atlas and patient's images. In this study, we focus on the automatic estimation of these parameters, pertaining to tumor simulation. In particular, we propose an objective function reflecting feature-based similarity and elastic stretching energy and optimize it with APPSPACK (Asynchronous Parallel Pattern Search), for achieving significant reduction of the computational cost. The results indicate that the registration accuracy is high in areas around the tumor, as well as in the healthy portion of the brain.

  16. Brain and Spinal Cord Tumors in Adults

    MedlinePlus

    ... Search Search En Español Category Cancer A-Z Brain and Spinal Cord Tumors in Adults If you have a brain or spinal cord tumor or are close to ... cope. Here you can find out all about brain and spinal cord tumors in adults, including risk ...

  17. 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

  18. Brain tumor locating in 3D MR volume using symmetry

    NASA Astrophysics Data System (ADS)

    Dvorak, Pavel; Bartusek, Karel

    2014-03-01

    This work deals with the automatic determination of a brain tumor location in 3D magnetic resonance volumes. The aim of this work is not the precise segmentation of the tumor and its parts but only the detection of its location. This work is the first step in the tumor segmentation process, an important topic in neuro-image processing. The algorithm expects 3D magnetic resonance volumes of brain containing a tumor. The detection is based on locating the area that breaks the left-right symmetry of the brain. This is done by multi-resolution comparing of corresponding regions in left and right hemisphere. The output of the computation is the probabilistic map of the tumor location. The created algorithm was tested on 80 volumes from publicly available BRATS databases containing 3D brain volumes afflicted by a brain tumor. These pathological structures had various sizes and shapes and were located in various parts of the brain. The locating performance of the algorithm was 85% for T1-weighted volumes, 91% for T1-weighted contrast enhanced volumes, 96% for FLAIR and T2-wieghted volumes and 95% for their combinations.

  19. 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.

  20. Neonatal brain image segmentation in longitudinal MRI studies.

    PubMed

    Shi, Feng; Fan, Yong; Tang, Songyuan; Gilmore, John H; Lin, Weili; Shen, Dinggang

    2010-01-01

    In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.

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

    MedlinePlus

    ... Diagnosis, and Staging Survival Rates for Selected Childhood Brain and Spinal Cord Tumors Survival rates are often ... Childhood Brain and Spinal Cord Tumors More In Brain and Spinal Cord Tumors in Children About Brain ...

  2. Fractal analysis of tumoral lesions in brain.

    PubMed

    Martín-Landrove, Miguel; Pereira, Demian; Caldeira, María E; Itriago, Salvador; Juliac, María

    2007-01-01

    In this work, it is proposed a method for supervised characterization and classification of tumoral lesions in brain, based on the analysis of irregularities at the lesion contour on T2-weighted MR images. After the choice of a specific image, a segmentation procedure with a threshold selected from the histogram of intensity levels is applied to isolate the lesion, the contour is detected through the application of a gradient operator followed by a conversion to a "time series" using a chain code procedure. The correlation dimension is calculated and analyzed to discriminate between normal or malignant structures. The results found showed that it is possible to detect a differentiation between benign (cysts) and malignant (gliomas) lesions suggesting the potential of this method as a diagnostic tool.

  3. Segmentation of brain structures in presence of a space-occupying lesion.

    PubMed

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

    2005-02-15

    Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

  4. Stereotaxic interstitial irradiation of malignant brain tumors

    SciTech Connect

    Gutin, P.H.; Leibel, S.A.

    1985-11-01

    The authors discuss the feasibility of treatment of malignant tumors with brachytherapy. The history of brain tumor brachytherapy, its present day use, and future directions are detailed. 24 references.

  5. Brain Tumor-Related Epilepsy

    PubMed Central

    Maschio, Marta

    2012-01-01

    In patients with brain tumor (BT), seizures are the onset symptom in 20-40% of patients, while a further 20-45% of patients will present them during the course of the disease. These patients present a complex therapeutic profile and require a unique and multidisciplinary approach. The choice of antiepileptic drugs is challenging for this particular patient population because brain tumor-related epilepsy (BTRE) is often drug-resistant, has a strong impact on the quality of life and weighs heavily on public health expenditures. In BT patients, the presence of epilepsy is considered the most important risk factor for long-term disability. For this reason, the problem of the proper administration of medications and their potential side effects is of great importance, because good seizure control can significantly improve the patient’s psychological and relational sphere. In these patients, new generation drugs such as gabapentin, lacosamide, levetiracetam, oxcarbazepine, pregabalin, topiramate, zonisamide are preferred because they have fewer drug interactions and cause fewer side effects. Among the recently marketed drugs, lacosamide has demonstrated promising results and should be considered a possible treatment option. Therefore, it is necessary to develop a customized treatment plan for each individual patient with BTRE. This requires a vision of patient management concerned not only with medical therapies (pharmacological, surgical, radiological, etc.) but also with emotional and psychological support for the individual as well as his or her family throughout all stages of the illness. PMID:23204982

  6. Lung tumor segmentation in PET images using graph cuts.

    PubMed

    Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

    2013-03-01

    The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

  7. Drug delivery systems for brain tumor therapy.

    PubMed

    Rautioa, Jarkko; Chikhale, Prashant J

    2004-01-01

    Brain tumors are one of the most lethal forms of cancer. They are extremely difficult to treat. Although, the rate of brain tumor incidence is relatively low, the field clearly lacks therapeutic strategies capable of overcoming barriers for effective delivery of drugs to brain tumors. Clinical failure of many potentially effective therapeutics for the treatment of brain tumors is usually not due to a lack of drug potency, but rather can be attributed to shortcomings in the methods by which a drug is delivered to the brain and into brain tumors. In response to the lack of efficacy of conventional drug delivery methods, extensive efforts have been made to develop novel strategies to overcome the obstacles for brain tumor drug delivery. The challenge is to design therapeutic strategies that deliver drugs to brain tumors in a safe and effective manner. This review provides some insight into several potential techniques that have been developed to improve drug delivery to brain tumors, and it should be helpful to clinicians and research scientists as well.

  8. Automatic brain tumor detection in MRI: methodology and statistical validation

    NASA Astrophysics Data System (ADS)

    Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

    2005-04-01

    Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

  9. Phase congruency map driven brain tumour segmentation

    NASA Astrophysics Data System (ADS)

    Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

    2015-03-01

    Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

  10. Automatic brain tumor extraction from T1-weighted coronal MRI using fast bounding box and dynamic snake.

    PubMed

    Xu, Tao; Mandal, Mrinal

    2012-01-01

    Brain tumor segmentation from MRI data is an important but challenging task. This paper presents an efficient and fully automatic brain tumor segmentation technique. The proposed technique includes a fuzzy C-means (FCM) based preprocessing to enhance the quality of T1-weighted coronal MR images, a fast bounding box (FBB) detection algorithm to locate a rectangle around tumor, and a new dynamic snake using modified Hausdorff distance (MHD) for the final tumor extraction.

  11. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

    PubMed Central

    Despotović, Ivana

    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. PMID:25945121

  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. 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

  14. The Microenvironmental Landscape of Brain Tumors.

    PubMed

    Quail, Daniela F; Joyce, Johanna A

    2017-03-13

    The brain tumor microenvironment (TME) is emerging as a critical regulator of cancer progression in primary and metastatic brain malignancies. The unique properties of this organ require a specific framework for designing TME-targeted interventions. Here, we discuss a number of these distinct features, including brain-resident cell types, the blood-brain barrier, and various aspects of the immune-suppressive environment. We also highlight recent advances in therapeutically targeting the brain TME in cancer. By developing a comprehensive understanding of the complex and interconnected microenvironmental landscape of brain malignancies we will greatly expand the range of therapeutic strategies available to target these deadly diseases.

  15. Tumor growth model for atlas based registration of pathological brain MR images

    NASA Astrophysics Data System (ADS)

    Moualhi, Wafa; Ezzeddine, Zagrouba

    2015-02-01

    The motivation of this work is to register a tumor brain magnetic resonance (MR) image with a normal brain atlas. A normal brain atlas is deformed in order to take account of the presence of a large space occupying tumor. The method use a priori model of tumor growth assuming that the tumor grows in a radial way from a starting point. First, an affine transformation is used in order to bring the patient image and the brain atlas in a global correspondence. Second, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. Finally, the seeded atlas is deformed combining a method derived from optical flow principles and a model for tumor growth (MTG). Results show that an automatic segmentation method of brain structures in the presence of large deformation can be provided.

  16. Combining Multi-atlas Segmentation with Brain Surface Estimation.

    PubMed

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

    2016-02-27

    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 population.

  17. 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.

  18. 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

  19. 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".

  20. Embryonal brain tumors and developmental control genes

    SciTech Connect

    Aguzzi, A.

    1995-12-31

    Cell proliferation in embryogenesis and neoplastic transformation is thought to be controlled by similar sets of regulatory genes. This is certainly true for tumors of embryonic origin, such as Ewing sarcoma, Wilms` tumor and retinoblastoma, in which developmental control genes are either activated as oncogenes to promote proliferation, or are inactivated to eliminate their growth suppressing function. However, to date little is known about the genetic events underlying the pathogenesis of medulloblastoma, the most common brain tumor in children, which still carries an unfavourable prognosis. None of the common genetic alterations identified in other neuroectodermal tumors, such as mutation of the p53 gene or amplification of tyrosine kinase receptor genes, could be uncovered as key events in the formation of medulloblastoma. The identification of regulatory genes which are expressed in this pediatric brain tumor may provide an alternative approach to gain insight into the molecular aspects of tumor formation.

  1. 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.

  2. The proteomics of pediatric brain tumors.

    PubMed

    Anagnostopoulos, Athanasios K; Tsangaris, George T

    2014-10-01

    Pediatric tumors of the CNS are the leading cause of cancer-related mortality in children. In pediatric pathology, brain tumors constitute the most frequent solid malignancy. An unparalleled outburst of information in pediatric neuro-oncology research has been witnessed over the last few years, largely due to increased use of high-throughput technologies such as genomics, proteomics and meta-analysis tools. Input from these technologies gives scientists the advantage of early prognosis assessment, more accurate diagnosis and prospective curative intent in the pediatric brain tumor clinical setting. The present review aims to summarize current knowledge on research applying proteomics techniques or proteomics-based approaches performed on pediatric brain tumors. Proteins that can be used as potential disease markers or molecular targets, and their biological significance, are herein listed and discussed. Furthermore, future perspectives that proteomics technologies may offer regarding this devastating disorder are presented.

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

    MedlinePlus

    ... Children Early Detection, Diagnosis, and Staging 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 ...

  4. Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis

    PubMed Central

    Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Lin, Weili; Shen, Dinggang

    2016-01-01

    Segmentation of isointense infant brain (at ~6-months-old) MR images is challenging due to the ongoing maturation and myelination process in the first year of life. In particular, signal contrast between white and gray matters inverses around 6 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, thus posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenges based on stacked kernel canonical correlation analysis (KCCA). Our main idea is to utilize the 12-month-old brain image with high tissue contrast to guide the segmentation of 6-month-old brain images with extremely low contrast. Specifically, we use KCCA to learn the common feature representations for both 6-month-old and the subsequent 12-month-old brain images of same subjects to make their features comparable in the common space. Note that the longitudinal 12-month-old brain images are not required in the testing stage, and they are required only in the KCCA based training stage to provide a set of longitudinal 6- and 12-month-old image pairs for training. Moreover, for optimizing the common feature representations, we propose a stacked KCCA mapping, instead of using only the conventional one-step of KCCA mapping. In this way, we can better use the 12-month-old brain images as multiple atlases to guide the segmentation of isointense brain images. Specifically, sparse patch-based multi-atlas labeling is used to propagate tissue labels in the (12-month-old) atlases and segment isointense brain images by measuring patch similarity between testing and atlas images with their learned common features. The proposed method was evaluated on 20 isointense brain images via leave-one-out cross-validation, showing much better performance than the state-of-the-art methods.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. Psychiatric aspects of brain tumors: A review.

    PubMed

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

    2015-09-22

    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.

  12. Confronting pediatric brain tumors: parent stories.

    PubMed

    McMillan, Gigi

    2014-01-01

    This narrative symposium brings to light the extreme difficulties faced by parents of children diagnosed with brain tumors. NIB editorial staff and narrative symposium editors, Gigi McMillan and Christy A. Rentmeester, developed a call for stories that was distributed on several list serves and posted on Narrative Inquiry in Bioethics' website. The call asks parents to share their personal experience of diagnosis, treatment, long-term effects of treatment, social issues and the doctor-patient-parent dynamic that develops during this process. Thirteen stories are found in the print version of the journal and an additional six supplemental stories are published online only through Project MUSE. One change readers may notice is that the story authors are not listed in alphabetical order. The symposium editors had a vision for this issue that included leading readers through the timeline of this topic: diagnosis-treatment-acute recovery-recurrence-treatment (again)-acute recovery (again)-long-term quality of life-(possibly) end of life. Stories are arranged to help lead the reader through this timeline.Gigi McMillan is a patient and research subject advocate, co-founder of We Can, Pediatric Brain Tumor Network, as well as, the mother of a child who suffered from a pediatric brain tumor. She also authored the introduction for this symposium. Christy Rentmeester is an Associate Professor of Health Policy and Ethics in the Creighton University School of Medicine. She served as a commentator for this issue. Other commentators for this issue are Michael Barraza, a clinical psychologist and board member of We Can, Pediatric Brain Tumor Network; Lisa Stern, a pediatrician who has diagnosed six children with brain tumors in her 20 years of practice; and Katie Rose, a pediatric brain tumor patient who shares her special insights about this world.

  13. Neurologic sequelae of brain tumors in children.

    PubMed

    Ullrich, Nicole J

    2009-11-01

    Neurologic signs and symptoms are often the initial presenting features of a primary brain tumor and may also emerge during the course of therapy or as late effects of the tumor and its treatment. Variables that influence the development of such neurologic complications include the type, size, and location of the tumor, the patient's age at diagnosis, and the treatment modalities used. Heightened surveillance and improved neuroimaging modalities have been instrumental in detecting and addressing such complications, which are often not appreciated until many years after completion of therapy. As current brain tumor therapies are continually refined and newer targeted therapies are developed, it will be important for future cooperative group studies to include systematic assessments to determine the incidence of neurologic complications and to provide a framework for the development of novel strategies for prevention and intervention.

  14. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy.

    PubMed

    Vaidyanathan, M; Clarke, L P; Velthuizen, R P; Phuphanich, S; Bensaid, A M; Hall, L O; Bezdek, J C; Greenberg, H; Trotti, A; Silbiger, M

    1995-01-01

    Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T1-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T1-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.

  15. Comprehensive brain MRI segmentation in high risk preterm newborns.

    PubMed

    Yu, Xintian; Zhang, Yanjie; Lasky, Robert E; Datta, Sushmita; Parikh, Nehal A; Narayana, Ponnada A

    2010-11-08

    Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes.

  16. 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.

  17. Metabolism of steroids by human brain tumors.

    PubMed

    Weidenfeld, J; Schiller, H

    1984-01-01

    Hormonal steroids or their precursors can be metabolized in the CNS to products with altered hormonal activity. The importance of the intracerebral transformation of steroids has been demonstrated, particularly with regard to neuroendocrine regulation and sexual behavior. These studies were carried out on normal brain tissues, but the ability of neoplastic tissues of CNS origin to metabolize steroids is unknown. We investigated the in vitro metabolism of tritiated pregnenolone, testosterone, and estradiol-17 beta by homogenates of four brain tumors defined as astrocytomas. In three tumors of cortical origin, removed from adult patients, the only enzymic activity found was the conversion of estradiol to estrone. In one tumor of cerebellar origin removed from an 11-year-old boy, the following conversions were found: pregnenolone to progesterone, testosterone to either androstenedione or estradiol, and estradiol to estrone. These results demonstrate that human astrocytomas can transform steroids to compounds with modified hormonal activity. These compounds formed by the tumorous tissue can affect brain function, which may be of clinical significance. Furthermore, these results may add important parameters for biochemical characterization of neoplastic brain tissues.

  18. 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.

  19. 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.

  20. Ion transporters in brain tumors

    PubMed Central

    Cong, Damin; Zhu, Wen; Kuo, John S.; Hu, Shaoshan; Sun, Dandan

    2015-01-01

    Ion transporters are important in regulation of ionic homeostasis, cell volume, and cellular signal transduction under physiological conditions. They have recently emerged as important players in cancer progression. In this review, we discussed two important ion transporter proteins, sodium-potassium-chloride cotransporter isoform 1 (NKCC-1) and sodium-hydrogen exchanger isoform 1 (NHE-1) in Glioblastoma multiforme (GBM) and other malignant tumors. NKCC-1 is a Na+-dependent Cl− transporter that mediates the movement of Na+, K+, and Cl− ions across the plasma membrane and maintains cell volume and intracellular K+ and Cl− homeostasis. NHE-1 is a ubiquitously expressed cell membrane protein which regulates intracellular pH (pHi) and extracellular microdomain pH (pHe) homeostasis and cell volume. Here, we summarized recent pre-clinical experimental studies on NKCC-1 and NHE-1 in GBM and other malignant tumors, such as breast cancer, hepatocellular carcinoma, and lung cancer. These studies illustrated that pharmacological inhibition or down-regulation of these ion transporter proteins reduces proliferation, increases apoptosis, and suppresses migration and invasion of cancer cells. These new findings reveal the potentials of these ion transporters as new targets for cancer diagnosis and/or treatment. PMID:25620102

  1. Brain tumors: Special characters for research and banking

    PubMed Central

    Kheirollahi, Majid; Dashti, Sepideh; Khalaj, Zahra; Nazemroaia, Fatemeh; Mahzouni, Parvin

    2015-01-01

    A brain tumor is an intracranial neoplasm within the brain or in the central spinal canal. Primary malignant brain tumors affect about 200,000 people worldwide every year. Brain cells have special characters. Due to the specific properties of brain tumors, including epidemiology, growth, and division, investigation of brain tumors and the interpretation of results is not simple. Research to identify the genetic alterations of human tumors improves our knowledge of tumor biology, genetic interactions, progression, and preclinical therapeutic assessment. Obtaining data for prevention, diagnosis, and therapy requires sufficient samples, and brain tumors have a wide range. As a result, establishing the bank of brain tumors is very important and essential. PMID:25625110

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

    PubMed

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

    2013-07-07

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  4. 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

  5. Neurocutaneous Syndromes and Brain Tumors.

    PubMed

    Ullrich, Nicole J

    2016-10-01

    The etiology of most childhood cancer remains largely unknown, but is likely attributable to random or induced genetic aberrations in somatic tissue. However, a subset of children develops cancer in the setting of an underlying inheritable condition involving a germline genetic mutation or chromosomal aberration. The term "neurocutaneous syndrome" encompasses a group of multisystem, hereditary disorders that are associated with skin manifestations as well as central and/or peripheral nervous system lesions of variable severity. This review outlines the central nervous system tumors associated with underlying neurocutaneous disorders, including neurofibromatosis type 1, neurofibromatosis type 2, schwannomatosis, tuberous sclerosis complex, Von Hippel Lindau, and nevoid basal cell carcinoma syndrome. Recognizing the presence of an underlying syndrome is critically important to both optimizing clinical care and treatment as well as genetic counseling and monitoring of these affected patients and their families.

  6. 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.

  7. 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).

  8. 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.

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

    PubMed

    Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J 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. Initial clinical results are presented on 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. 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, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

  10. 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

  11. 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.

  12. Brain Tumor Epidemiology: Consensus from the Brain Tumor Epidemiology Consortium (BTEC)

    PubMed Central

    Bondy, Melissa L.; Scheurer, Michael E.; Malmer, Beatrice; Barnholtz-Sloan, Jill S.; Davis, Faith G.; Il’yasova, Dora; Kruchko, Carol; McCarthy, Bridget J.; Rajaraman, Preetha; Schwartzbaum, Judith A.; Sadetzki, Siegal; Schlehofer, Brigitte; Tihan, Tarik; Wiemels, Joseph L.; Wrensch, Margaret; Buffler, Patricia A.

    2010-01-01

    Epidemiologists in the Brain Tumor Epidemiology Consortium (BTEC) have prioritized areas for further research. Although many risk factors have been examined over the past several decades, there are few consistent findings possibly due to small sample sizes in individual studies and differences between studies in subjects, tumor types, and methods of classification. Individual studies have generally lacked sufficient sample size to examine interactions. A major priority based on available evidence and technologies includes expanding research in genetics and molecular epidemiology of brain tumors. BTEC has taken an active role in promoting understudied groups such as pediatric brain tumors, the etiology of rare glioma subtypes, such as oligodendroglioma, and meningioma, which not uncommon, has only recently been systematically registered in the US. There is also a pressing need to bring more researchers, especially junior investigators, to study brain tumor epidemiology. However, relatively poor funding for brain tumor research has made it difficult to encourage careers in this area. We review the group’s consensus on the current state of scientific findings and present a consensus on research priorities to identify the important areas the science should move to address. PMID:18798534

  13. Asymmetric bias in user guided segmentations of brain structures

    NASA Astrophysics Data System (ADS)

    Styner, Martin; Smith, Rachel G.; Graves, Michael M.; Mosconi, Matthew W.; Peterson, Sarah; White, Scott; Blocher, Joe; El-Sayed, Mohammed; Hazlett, Heather C.

    2007-03-01

    Brain morphometric studies often incorporate comparative asymmetry analyses of left and right hemispheric brain structures. In this work we show evidence that common methods of user guided structural segmentation exhibit strong left-right asymmetric biases and thus fundamentally influence any left-right asymmetry analyses. We studied several structural segmentation methods with varying degree of user interaction from pure manual outlining to nearly fully automatic procedures. The methods were applied to MR images and their corresponding left-right mirrored images from an adult and a pediatric study. Several expert raters performed the segmentations of all structures. The asymmetric segmentation bias is assessed by comparing the left-right volumetric asymmetry in the original and mirrored datasets, as well as by testing each sides volumetric differences to a zero mean standard t-tests. The structural segmentations of caudate, putamen, globus pallidus, amygdala and hippocampus showed a highly significant asymmetric bias using methods with considerable manual outlining or landmark placement. Only the lateral ventricle segmentation revealed no asymmetric bias due to the high degree of automation and a high intensity contrast on its boundary. Our segmentation methods have been adapted in that they are applied to only one of the hemispheres in an image and its left-right mirrored image. Our work suggests that existing studies of hemispheric asymmetry without similar precautions should be interpreted in a new, skeptical light. Evidence of an asymmetric segmentation bias is novel and unknown to the imaging community. This result seems less surprising to the visual perception community and its likely cause is differences in perception of oppositely curved 3D structures.

  14. 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.

  15. Unarmed, tumor-specific monoclonal antibody effectively treats brain tumors

    PubMed Central

    Sampson, John H.; Crotty, Laura E.; Lee, Samson; Archer, Gary E.; Ashley, David M.; Wikstrand, Carol J.; Hale, Laura P.; Small, Clayton; Dranoff, Glenn; Friedman, Allan H.; Friedman, Henry S.; Bigner, Darell D.

    2000-01-01

    The epidermal growth factor receptor (EGFR) is often amplified and rearranged structurally in tumors of the brain, breast, lung, and ovary. The most common mutation, EGFRvIII, is characterized by an in-frame deletion of 801 base pairs, resulting in the generation of a novel tumor-specific epitope at the fusion junction. A murine homologue of the human EGFRvIII mutation was created, and an IgG2a murine mAb, Y10, was generated that recognizes the human and murine equivalents of this tumor-specific antigen. In vitro, Y10 was found to inhibit DNA synthesis and cellular proliferation and to induce autonomous, complement-mediated, and antibodydependent cell-mediated cytotoxicity. Systemic treatment with i.p. Y10 of s.c. B16 melanomas transfected to express stably the murine EGFRvIII led to long-term survival in all mice treated (n = 20; P < 0.001). Similar therapy with i.p. Y10 failed to increase median survival of mice with EGFRvIII-expressing B16 melanomas in the brain; however, treatment with a single intratumoral injection of Y10 increased median survival by an average 286%, with 26% long-term survivors (n = 117; P < 0.001). The mechanism of action of Y10 in vivo was shown to be independent of complement, granulocytes, natural killer cells, and T lymphocytes through in vivo complement and cell subset depletions. Treatment with Y10 in Fc receptor knockout mice demonstrated the mechanism of Y10 to be Fc receptor-dependent. These data indicate that an unarmed, tumor-specific mAb may be an effective immunotherapy against human tumors and potentially other pathologic processes in the “immunologically privileged” central nervous system. PMID:10852962

  16. Perspectives on Dual Targeting Delivery Systems for Brain Tumors.

    PubMed

    Gao, Huile

    2017-03-01

    Brain tumor remains one of the most serious threats to human beings. Different from peripheral tumors, drug delivery to brain tumor is largely restricted by the blood brain barrier (BBB). To fully conquer this barrier and specifically deliver drugs to brain tumor, dual targeting delivery systems were explored, which are functionalized with two active targeting ligands: one to the BBB and the other to the brain tumor. The development of dual targeting delivery system is still in its early stage, and attentions need to be paid to issues and concerns that remain unresolved in future studies.

  17. Brain Tumors - Multiple Languages: MedlinePlus

    MedlinePlus

    ... List of All Topics All Brain Tumors - Multiple Languages To use the sharing features on this page, please enable JavaScript. French (français) Japanese (日本語) Korean (한국어) Russian (Русский) Somali (af Soomaali) Spanish (español) Ukrainian (Українська) ...

  18. 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.

  19. [Chemotherapy of brain tumors in aduts].

    PubMed

    Roth, P; Weller, M

    2015-04-01

    The treatment of patients with brain tumors has long been the domain of neurosurgery and radiotherapy but chemotherapy is now well established as an additional treatment option for many tumor entities in neuro-oncology. This is particularly true for patients with newly diagnosed and relapsing glioblastoma and anaplastic glioma as well as the treatment of medulloblastoma and primary lymphoma of the central nervous system (CNS). In addition to purely histopathological features, treatment decisions including those for chemotherapy are now based increasingly more on molecular tumor profiling. Within the group of gliomas these markers include the methylation status of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the 1p/19q status, which reflects the loss of genetic material on chromosome arms 1p and 19q. The presence of a 1p/19q codeletion is associated with a better prognosis and increased sensitivity to alkylating chemotherapy in patients with anaplastic gliomas.

  20. Atlas-guided segmentation of vervet monkey brain MRI.

    PubMed

    Fedorov, Andriy; Li, Xiaoxing; Pohl, Kilian M; Bouix, Sylvain; Styner, Martin; Addicott, Merideth; Wyatt, Chris; Daunais, James B; Wells, William M; Kikinis, Ron

    2011-01-01

    The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model.

  1. Atlas-Guided Segmentation of Vervet Monkey Brain MRI

    PubMed Central

    Fedorov, Andriy; Li, Xiaoxing; Pohl, Kilian M; Bouix, Sylvain; Styner, Martin; Addicott, Merideth; Wyatt, Chris; Daunais, James B; Wells, William M; Kikinis, Ron

    2011-01-01

    The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model. PMID:22253661

  2. 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.

  3. Brain blood vessel segmentation using line-shaped profiles.

    PubMed

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

    2013-11-21

    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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Normal brain volume measurements using multispectral MRI segmentation.

    PubMed

    Vaidyanathan, M; Clarke, L P; Heidtman, C; Velthuizen, R P; Hall, L O

    1997-01-01

    The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

  9. 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

  10. A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning

    PubMed Central

    Mostaar, Ahmad; Houshyari, Mohammad; Badieyan, Saeedeh

    2016-01-01

    Introduction Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense inhomogeneity with the ability to determine the position of contour and energy diagram. Methods A Gaussian distribution model with different means and variances was used for inhomogeneity, and a moving window was used to map the original image into another domain in which the intensity distributions of inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as visualization and energy functions. Results In the proposed method, we were able to determine the size and position of the initial contour and to count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The energy function was reduced by about 5 and 7% after 70 and 430 iterations, respectively. These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that we define for the energy equation. Conclusion An active contour model based on the energy function is a useful tool for medical image segmentation. The proposed method combined the information about neighboring pixels that

  11. 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.

  12. Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation.

    PubMed

    Fakhry, Ahmed; Zeng, Tao; Ji, Shuiwang

    2017-02-01

    Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.

  13. The Brain's Cutting-Room Floor: Segmentation of Narrative Cinema

    PubMed Central

    Zacks, Jeffrey M.; Speer, Nicole K.; Swallow, Khena M.; Maley, Corey J.

    2010-01-01

    Observers segment ongoing activity into meaningful events. Segmentation is a core component of perception that helps determine memory and guide planning. The current study tested the hypotheses that event segmentation is an automatic component of the perception of extended naturalistic activity, and that the identification of event boundaries in such activities results in part from processing changes in the perceived situation. Observers may identify boundaries between events as a result of processing changes in the observed situation. To test this hypothesis and study this potential mechanism, we measured brain activity while participants viewed an extended narrative film. Large transient responses were observed when the activity was segmented, and these responses were mediated by changes in the observed activity, including characters and their interactions, interactions with objects, spatial location, goals, and causes. These results support accounts that propose event segmentation is automatic and depends on processing meaningful changes in the perceived situation; they are the first to show such effects for extended naturalistic human activity. PMID:20953234

  14. The Brain's Cutting-Room Floor: Segmentation of Narrative Cinema.

    PubMed

    Zacks, Jeffrey M; Speer, Nicole K; Swallow, Khena M; Maley, Corey J

    2010-01-01

    Observers segment ongoing activity into meaningful events. Segmentation is a core component of perception that helps determine memory and guide planning. The current study tested the hypotheses that event segmentation is an automatic component of the perception of extended naturalistic activity, and that the identification of event boundaries in such activities results in part from processing changes in the perceived situation. Observers may identify boundaries between events as a result of processing changes in the observed situation. To test this hypothesis and study this potential mechanism, we measured brain activity while participants viewed an extended narrative film. Large transient responses were observed when the activity was segmented, and these responses were mediated by changes in the observed activity, including characters and their interactions, interactions with objects, spatial location, goals, and causes. These results support accounts that propose event segmentation is automatic and depends on processing meaningful changes in the perceived situation; they are the first to show such effects for extended naturalistic human activity.

  15. 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.

  16. 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)

  17. 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

  18. Multimodal navigated skull base tumor resection using image-based vascular and cranial nerve segmentation: A prospective pilot study

    PubMed Central

    Dolati, Parviz; Gokoglu, Abdulkerim; Eichberg, Daniel; Zamani, Amir; Golby, Alexandra; Al-Mefty, Ossama

    2015-01-01

    Background: Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge. Methods: To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured. Results: Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases. Conclusion: Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients. PMID:26674155

  19. 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.

  20. 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

  1. Computer aided detection of tumor and edema in brain FLAIR magnetic resonance image using ANN

    NASA Astrophysics Data System (ADS)

    Pradhan, Nandita; Sinha, A. K.

    2008-03-01

    This paper presents an efficient region based segmentation technique for detecting pathological tissues (Tumor & Edema) of brain using fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. This work segments FLAIR brain images for normal and pathological tissues based on statistical features and wavelet transform coefficients using k-means algorithm. The image is divided into small blocks of 4×4 pixels. The k-means algorithm is used to cluster the image based on the feature vectors of blocks forming different classes representing different regions in the whole image. With the knowledge of the feature vectors of different segmented regions, supervised technique is used to train Artificial Neural Network using fuzzy back propagation algorithm (FBPA). Segmentation for detecting healthy tissues and tumors has been reported by several researchers by using conventional MRI sequences like T1, T2 and PD weighted sequences. This work successfully presents segmentation of healthy and pathological tissues (both Tumors and Edema) using FLAIR images. At the end pseudo coloring of segmented and classified regions are done for better human visualization.

  2. Brain tumor imaging: imaging brain metastasis using a brain-metastasizing breast adenocarcinoma.

    PubMed

    Madden, Kelley S; Zettel, Martha L; Majewska, Ania K; Brown, Edward B

    2013-03-01

    Brain metastases from primary or secondary breast tumors are difficult to model in the mouse. When metastatic breast cancer cell lines are injected directly into the arterial circulation, only a small fraction of cells enter the brain to form metastatic foci. To study the molecular and cellular mechanisms of brain metastasis, we have transfected MB-231BR, a brain-homing derivative of a human breast adenocarcinoma line MDA-MB-231, with the yellow fluorescent protein (YFP) variant Venus. MB-231BR selectively enters the brain after intracardiac injection into the arterial circulation, resulting in accumulation of fluorescent foci of cells in the brain that can be viewed by standard fluorescence imaging procedures. We describe how to perform the intracardiac injection and the parameters used to quantify brain metastasis in brain sections by standard one-photon fluorescence imaging. The disadvantage of this model is that the kinetics of growth over time cannot be determined in the same animal. In addition, the injection technique does not permit precise placement of tumor cells within the brain. This model is useful for determining the molecular determinants of brain tumor metastasis.

  3. Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

    NASA Astrophysics Data System (ADS)

    Dvořák, P.; Kropatsch, W. G.; Bartušek, K.

    2013-10-01

    This work focuses on fully automatic detection of brain tumors. The first aim is to determine, whether the image contains a brain with a tumor, and if it does, localize it. The goal of this work is not the exact segmentation of tumors, but the localization of their approximate position. The test database contains 203 T2-weighted images of which 131 are images of healthy brain and the remaining 72 images contain brain with pathological area. The estimation, whether the image shows an afflicted brain and where a pathological area is, is done by multi resolution symmetry analysis. The first goal was tested by five-fold cross-validation technique with 100 repetitions to avoid the result dependency on sample order. This part of the proposed method reaches the true positive rate of 87.52% and the true negative rate of 93.14% for an afflicted brain detection. The evaluation of the second part of the algorithm was carried out by comparing the estimated location to the true tumor location. The detection of the tumor location reaches the rate of 95.83% of correct anomaly detection and the rate 87.5% of correct tumor location.

  4. Prior knowledge driven multiscale segmentation of brain MRI.

    PubMed

    Akselrod-Ballin, Ayelet; Galun, Meirav; Gomori, John Moshe; Brandt, Achi; Basri, Ronen

    2007-01-01

    We present a novel automatic multiscale algorithm applied to segmentation of anatomical structures in brain MRI. The algorithm which is derived from algebraic multigrid, uses a graph representation of the image and performs a coarsening process that produces a full hierarchy of segments. Our main contribution is the incorporation of prior knowledge information into the multiscale framework through a Bayesian formulation. The probabilistic information is based on an atlas prior and on a likelihood function estimated from a manually labeled training set. The significance of our new approach is that the constructed pyramid, reflects the prior knowledge formulated. This leads to an accurate and efficient methodology for detection of various anatomical structures simultaneously. Quantitative validation results on gold standard MRI show the benefit of our approach.

  5. 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.

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

    PubMed Central

    1986-01-01

    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. Images FIGURE 1. FIGURE 2. FIGURE 3. FIGURE 4. FIGURE 5. PMID:3536473

  7. Growth patterns of microscopic brain tumors

    NASA Astrophysics Data System (ADS)

    Sander, Leonard M.; Deisboeck, Thomas S.

    2002-11-01

    Highly malignant brain tumors such as glioblastoma multiforme form complex growth patterns in vitro in which invasive cells organize in tenuous branches. Here, we formulate a chemotaxis model for this sort of growth. A key element controlling the pattern is homotype attraction, i.e., the tendency for invasive cells to follow pathways previously explored. We investigate this in two ways: we show that there is an intrinsic instability in the model, which leads to branch formation. We also give a discrete description for the expansion of the invasive zone, and a continuum model for the nutrient supply. The results indicate that both strong heterotype chemotaxis and strong homotype chemoattraction are required for branch formation within the invasive zone. Our model thus can give a way to assess the importance of the various processes, and a way to explore and analyze transitions between different growth regimes.

  8. Neural stem cell-based gene therapy for brain tumors.

    PubMed

    Kim, Seung U

    2011-03-01

    Advances in gene-based medicine since 1990s have ushered in new therapeutic strategy of gene therapy for inborn error genetic diseases and cancer. Malignant brain tumors such as glioblastoma multiforme and medulloblastoma remain virtually untreatable and lethal. Currently available treatment for brain tumors including radical surgical resection followed by radiation and chemotherapy, have substantially improved the survival rate in patients suffering from these brain tumors; however, it remains incurable in large proportion of patients. Therefore, there is substantial need for effective, low-toxicity therapies for patients with malignant brain tumors, and gene therapy targeting brain tumors should fulfill this requirement. Gene therapy for brain tumors includes many therapeutic strategies and these strategies can be grouped in two major categories: molecular and immunologic. The widely used molecular gene therapy approach is suicide gene therapy based on the conversion of non-toxic prodrugs into active anticancer agents via introduction of enzymes and genetic immunotherapy involves the gene transfer of immune-stimulating cytokines including IL-4, IL-12 and TRAIL. For both molecular and immune gene therapy, neural stem cells (NSCs) can be used as delivery vehicle of therapeutic genes. NSCs possess an inherent tumor tropism that supports their use as a reliable delivery vehicle to target therapeutic gene products to primary brain tumors and metastatic cancers throughout the brain. Significance of the NSC-based gene therapy for brain tumor is that it is possible to exploit the tumor-tropic property of NSCs to mediate effective, tumor-selective therapy for primary and metastatic cancers in the brain and outside, for which no tolerated curative treatments are currently available.

  9. Thermal imaging of brain tumors in a rat glioma model

    NASA Astrophysics Data System (ADS)

    Papaioannou, Thanassis; Thompson, Reid C.; Kateb, Babak; Sorokoumov, Oleg; Grundfest, Warren S.; Black, Keith L.

    2002-05-01

    We have explored the capability of thermal imaging for the detection of brain tumors in a rat glioma mode. Fourteen Wistar rats were injected stereotactically with 100,000 C6 glioma cells. Approximately one and two weeks post implantation, the rats underwent bilateral craniotomy and the exposed brain surface was imaged with a short wave thermal camera. Thermal images were obtained at both low (approximately 28.7 degree(s)C) and high (approximately 38 degree(s)C) core temperatures. Temperature gradients between the tumor site and the contralateral normal brain were calculated. Overall, the tumors appeared cooler than normal brain, for both high and low core temperatures. Average temperature difference between tumor and normal brain were maximal in more advanced tumors (two weeks) and at higher core temperatures. At one week (N equals 6), the average temperature gradient between tumor and normal sites was 0.1 degree(s)C and 0.2 degree(s)C at low and high core temperatures respectively (P(greater than)0.05). At two weeks (N equals 8), the average temperature gradient was 0.3 degree(s)C and 0.7 degree(s)C at low and high core temperatures respectively (P<0.05). We conclude that thermal imaging can detect temperature differences between tumor and normal brain tissue in this model, particularly in more advanced tumors. Thermal imaging may provide a novel means to identify brain tumors intraoperatively.

  10. Brain necrosis after radiotherapy for primary intracerebral tumor.

    PubMed

    Hohwieler, M L; Lo, T C; Silverman, M L; Freidberg, S R

    1986-01-01

    Radiotherapy is a standard postoperative treatment for cerebral glioma. We have observed the onset of symptoms related to brain necrosis, as opposed to recurrent tumor, in surviving patients. This has been manifest as dementia with a computed tomographic pattern of low density in the frontal lobe uninvolved with tumor, but within the field of radiotherapy. Two patients presented with mass lesions also unrelated to recurrent tumor. We question the necessity of full brain irradiation and suggest that radiotherapy techniques be altered to target the tumor and not encompass the entire brain.

  11. Yoga Therapy in Treating Patients With Malignant Brain Tumors

    ClinicalTrials.gov

    2017-01-17

    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

  12. Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

    PubMed

    Bach Cuadra, M; De Craene, M; Duay, V; Macq, B; Pollo, C; Thiran, J-Ph

    2006-12-01

    Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

  13. 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.

  14. Brain tumors in children with neurofibromatosis: additional neuropsychological morbidity?

    PubMed Central

    De Winter, A. E.; Moore, B. D.; Slopis, J. M.; Ater, J. L.; Copeland, D. R.

    1999-01-01

    Neurofibromatosis type 1 is a common autosomal dominant genetic disorder associated with numerous physical anomalies and an increased incidence of neuropsychological impairment. Tumors of the CNS occur in approximately 15% of children with neurofibromatosis, presenting additional risk for cognitive impairment. This study examines the impact of an additional diagnosis of brain tumor on the cognitive profile of children with neurofibromatosis. A comprehensive battery of neuropsychological tests was administered to 149 children with neurofibromatosis. Thirty-six of these children had a codiagnosis of brain tumor. A subset of 36 children with neurofibromatosis alone was matched with the group of children diagnosed with neurofibromatosis and brain tumor. Although mean scores of the neurofibromatosis plus brain tumor group were, in general, lower than those of the neurofibromatosis alone group, these differences were not statistically significant. Children in the neurofibromatosis plus brain tumor group who received cranial irradiation (n = 9) demonstrated weaker academic abilities than did children with brain tumor who had not received that treatment. These results suggest that neurofibromatosis is associated with impairments in cognitive functioning, but the severity of the problems is not significantly exacerbated by the codiagnosis of a brain tumor unless treatment includes cranial irradiation. PMID:11550319

  15. 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

  16. Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge.

    PubMed

    Išgum, Ivana; Benders, Manon J N L; Avants, Brian; Cardoso, M Jorge; Counsell, Serena J; Gomez, Elda Fischi; Gui, Laura; Hűppi, Petra S; Kersbergen, Karina J; Makropoulos, Antonios; Melbourne, Andrew; Moeskops, Pim; Mol, Christian P; Kuklisova-Murgasova, Maria; Rueckert, Daniel; Schnabel, Julia A; Srhoj-Egekher, Vedran; Wu, Jue; Wang, Siying; de Vries, Linda S; Viergever, Max A

    2015-02-01

    A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40 weeks corrected age, (ii) coronal scans acquired at 30 weeks corrected age and (iii) coronal scans acquired at 40 weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.

  17. Inference With Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences.

    PubMed

    Lin, Liang; Yang, Wei; Li, Chenglong; Tang, Jin; Cao, Xiaochun

    2015-10-29

    Segmenting organisms or tumors from medical data (e.g., computed tomography volumetric images, ultrasound, or magnetic resonance imaging images/image sequences) is one of the fundamental tasks in medical image analysis and diagnosis, and has received long-term attentions. This paper studies a novel computational framework of interactive segmentation for extracting liver tumors from image sequences, and it is suitable for different types of medical data. The main contributions are twofold. First, we propose a collaborative model to jointly formulate the tumor segmentation from two aspects: 1) region partition and 2) boundary presence. The two terms are complementary but simultaneously competing: the former extracts the tumor based on its appearance/texture information, while the latter searches for the palpable tumor boundary. Moreover, in order to adapt the data variations, we allow the model to be discriminatively trained based on both the seed pixels traced by the Lucas-Kanade algorithm and the scribbles placed by the user. Second, we present an effective inference algorithm that iterates to: 1) solve tumor segmentation using the augmented Lagrangian method and 2) propagate the segmentation across the image sequence by searching for distinctive matches between images. We keep the collaborative model updated during the inference in order to well capture the tumor variations over time. We have verified our system for segmenting liver tumors from a number of clinical data, and have achieved very promising results. The software developed with this paper can be found at http://vision.sysu.edu.cn/projects/med-interactive-seg/.

  18. Mindcontrol: A Web Application for Brain Segmentation Quality Control.

    PubMed

    Keshavan, Anisha; Datta, Esha; McDonough, Ian; Madan, Christopher R; Jordan, Kesshi; Henry, Roland G

    2017-03-29

    Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study.

  19. An adaptive window-setting scheme for segmentation of bladder tumor surface via MR cystography.

    PubMed

    Duan, Chaijie; Yuan, Kehong; Liu, Fanghua; Xiao, Ping; Lv, Guoqing; Liang, Zhengrong

    2012-07-01

    This paper proposes an adaptive window-setting scheme for noninvasive detection and segmentation of bladder tumor surface in T(1)-weighted magnetic resonance (MR) images. The inner border of the bladder wall is first covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods that are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by ten clinical T(1)-weighted MR image datasets (five volunteers and five patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10,491 windows, respectively. Such a large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This study inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system.

  20. 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.

  1. 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

  2. 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

  3. Efficacy of cabazitaxel in mouse models of pediatric brain tumors

    PubMed Central

    Girard, Emily; Ditzler, Sally; Lee, Donghoon; Richards, Andrew; Yagle, Kevin; Park, Joshua; Eslamy, Hedieh; Bobilev, Dmitri; Vrignaud, Patricia; Olson, James

    2015-01-01

    Background There is an unmet need in the treatment of pediatric brain tumors for chemotherapy that is efficacious, avoids damage to the developing brain, and crosses the blood-brain barrier. These experiments evaluated the efficacy of cabazitaxel in mouse models of pediatric brain tumors. Methods The antitumor activity of cabazitaxel and docetaxel were compared in flank and orthotopic xenograft models of patient-derived atypical teratoid rhabdoid tumor (ATRT), medulloblastoma, and central nervous system primitive neuroectodermal tumor (CNS-PNET). Efficacy of cabazitaxel and docetaxel were also assessed in the Smo/Smo spontaneous mouse medulloblastoma tumor model. Results This study observed significant tumor growth inhibition in pediatric patient-derived flank xenograft tumor models of ATRT, medulloblastoma, and CNS-PNET after treatment with either cabazitaxel or docetaxel. Cabazitaxel, but not docetaxel, treatment resulted in sustained tumor growth inhibition in the ATRT and medulloblastoma flank xenograft models. Patient-derived orthotopic xenograft models of ATRT, medulloblastoma, and CNS-PNET showed significantly improved survival with treatment of cabazitaxel. Conclusion These data support further testing of cabazitaxel as a therapy for treating human pediatric brain tumors. PMID:25140037

  4. Cytogenetics and molecular genetics of childhood brain tumors.

    PubMed Central

    Biegel, J. A.

    1999-01-01

    Considerable progress has been made toward improving survival for children with brain tumors, and yet there is still relatively little known regarding the molecular genetic events that contribute to tumor initiation or progression. Nonrandom patterns of chromosomal deletions in several types of childhood brain tumors suggest that the loss or inactivation of tumor suppressor genes are critical events in tumorigenesis. Deletions of chromosomal regions 10q, 11 and 17p, and example, are frequent events in medulloblastoma, whereas loss of a region within 22q11.2, which contains the INI1 gene, is involved in the development of atypical teratoid and rhabdoid tumors. A review of the cytogenetic and molecular genetic changes identified to date in childhood brain tumors will be presented. PMID:11550309

  5. 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.

  6. 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.

  7. Three-dimensional segmentation of the tumor in computed tomographic images of neuroblastoma.

    PubMed

    Deglint, Hanford J; Rangayyan, Rangaraj M; Ayres, Fábio J; Boag, Graham S; Zuffo, Marcelo K

    2007-09-01

    Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to therapy and in the planning of the delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children's Hospital. False-negative error rates of less than 12% were obtained in eight of 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist.

  8. Segmentation of liver and liver tumor for the Liver-Workbench

    NASA Astrophysics Data System (ADS)

    Zhou, Jiayin; Ding, Feng; Xiong, Wei; Huang, Weimin; Tian, Qi; Wang, Zhimin; Venkatesh, Sudhakar K.; Leow, Wee Kheng

    2011-03-01

    Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure which integrates two novel semi-automatic algorithms has been developed to segment 3D liver and liver tumors from multi-detector computed tomography (MDCT) images. The first module is to segment the liver volume using a flippingfree mesh deformation model. In each iteration, before mesh deformation, the algorithm detects and avoids possible flippings which will cause the self-intersection of the mesh and then the undesired segmentation results. After flipping avoidance, Laplacian mesh deformation is performed with various constraints in geometry and shape smoothness. In the second module, the segmented liver volume is used as the ROI and liver tumors are segmented by using support vector machines (SVMs)-based voxel classification and propagational learning. First a SVM classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumorcontaining slices were processed. The performance of the whole procedure was tested using 20 MDCT data sets and the results were promising: Nineteen liver volumes were successfully segmented out, with the mean relative absolute volume difference (RAVD), volume overlap error (VOE) and average symmetric surface distance (ASSD) to reference segmentation of 7.1%, 12.3% and 2.5 mm, respectively. For live tumors segmentation, the median RAVD, VOE and ASSD were 7.3%, 18.4%, 1.7 mm, respectively.

  9. 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

  10. Labeled Putrescine as a Probe in Brain Tumors

    NASA Astrophysics Data System (ADS)

    Volkow, Nora; Goldman, Stephen S.; Flamm, Eugene S.; Cravioto, Humberto; Wolf, Alfred P.; Brodie, Jonathan D.

    1983-08-01

    The polyamine metabolism of transplanted N-nitrosomethylurea-derived rat glioma was determined with radiolabeled putrescine used as a marker for malignancy. The uptake of putrescine in vivo was complete within 5 minutes and was specific for tumor tissue. The conversion of putrescine to spermine and other metabolites by the tumor was rapid, in contrast to the case for adjacent normal brain. These results suggest that putrescine labeled with carbon-11 may be used as a positron-emission tomographic tracer for the selective metabolic imaging of brain tumor and may be used in an appropriate model as a marker for tumor growth rate.

  11. Applications of nanotechnology to imaging and therapy of brain tumors.

    PubMed

    Mohs, Aaron M; Provenzale, James M

    2010-08-01

    In the past decade, numerous advances in the understanding of brain tumor physiology, tumor imaging, and tumor therapy have been attained. In some cases, these advances have resulted from refinements of pre-existing technologies (eg, improvements of contrast-enhanced magnetic resonance imaging). In other instances, advances have resulted from development of novel technologies. The development of nanomedicine (ie, applications of nanotechnology to the field of medicine) is an example of the latter. In this review, the authors explain the principles that underlay nanoparticle design and function as well as the means by which nanoparticles can be used for imaging and therapy of brain tumors.

  12. Fractal analysis of microvascular networks in malignant brain tumors.

    PubMed

    Di Ieva, Antonio

    2012-01-01

    Brain tumors are characterized by a microvascular network which differs from normal brain vascularity. Different tumors show individual angiogenic patterns. Microvascular heterogeneity can also be observed within a neoplastic histotype. It has been shown that quantification of neoplastic microvascular patterns could be used in combination with the histological grade for tumor characterization and to refine clinical prognoses, even if no objective parameters have yet been validated. To overcome the limits of the Euclidean approach, we employ fractal geometry to analyze the geometric complexity underlying the microangioarchitectural networks in brain tumors. We have developed a computer-aided fractal-based analysis for the quantification of the microvascular patterns in histological specimens and ultra-high-field (7-Tesla) magnetic resonance images. We demonstrate that the fractal parameters are valid estimators of microvascular geometrical complexity. Furthermore, our analysis allows us to demonstrate the high geometrical variability underlying the angioarchitecture of glioblastoma multiforme and to differentiate low-grade from malignant tumors in histological specimens and radiological images. Based on the results of this study, we speculate the existence of a gradient in the geometrical complexity of microvascular networks from those in the normal brain to those in malignant brain tumors. Here, we summarize a new methodology for the application of fractal analysis to the study of the microangioarchitecture of brain tumors; we further suggest this approach as a tool for quantifying and categorizing different neoplastic microvascular patterns and as a potential morphometric biomarker for use in clinical practice.

  13. 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.

  14. Clinical applications of choline PET/CT in brain tumors.

    PubMed

    Giovannini, Elisabetta; Lazzeri, Patrizia; Milano, Amalia; Gaeta, Maria Chiara; Ciarmiello, Andrea

    2015-01-01

    Malignant gliomas and metastatic tumors are the most common forms of brain tumors. From a clinical perspective, neuroimaging plays a significant role, in diagnosis, treatment planning, and follow-up. To date MRI is considered the current clinical gold standard for imaging, however, despite providing superior structural detail it features poor specificity in identifying viable tumors in brain treated with surgery, radiation, or chemotherapy. In the last years functional neuroimaging has become largely widespread thanks to the use of molecular tracers employed in cellular metabolism which has significantly improved the management of patients with brain tumors, especially in the post-treatment phase. Despite the considerable progress of molecular imaging in oncology its use in the diagnosis of brain tumors is still limited by a few wellknown technical problems. Because 18F-FDG, the most common radiotracer used in oncology, is avidly accumulated by normal cortex, the low tumor/background signal ratio makes it difficult to distinguish the tumor from normal surrounding tissues. By contrast, radiotracers with higher specificity for the tumor are labeled with a short half-life isotopes which restricts their use to those centers equipped with a cyclotron and radiopharmacy facility. 11C-choline has been reported as a suitable tracer for neuroimaging application. The recent availability of choline labeled with a long half-life radioisotope as 18F increases the possibility of studying this tracer's potential role in the staging of brain tumors. The present review focuses on the possible clinical applications of PET/CT with choline tracers in malignant brain tumors and brain metastases, with a special focus on malignant gliomas.

  15. Sex steroids in human brain tumors and breast cancer.

    PubMed

    von Schoultz, E; Bixo, M; Bäckström, T; Silfvenius, H; Wilking, N; Henriksson, R

    1990-02-15

    The concentrations of three sex steroids, estradiol, progesterone and testosterone, were analyzed by radioimmunoassay after celite chromatography in brain tumor and breast cancer tissues. The concentrations in malignant gliomas and breast cancers showed interindividual variations, especially evident with regard to estradiol. High estradiol concentrations were recorded in two patients with malignant astrocytoma. The concentrations of 1.00 pg/mg and 3.32 pg/mg were 10 to 30 times as high as in normal female brain. In five of ten astrocytomas the estradiol concentration was higher than the lowest breast cancer value. The distribution of progesterone seemed more even, and the level was significantly lower in brain tumors and breast cancers as compared with female brain, perhaps indicating an increased metabolism. Testosterone levels were somewhat higher in brain tumors, as compared with breast cancers, but not different from values in brain tissue. There were no significant age or sex correlation or differences in the concentrations of steroids in the brain tumors. The results suggest that manipulation of sex steroid metabolism in malignant brain tumors can be of beneficial therapeutic value as has been shown for breast cancer and prostatic carcinoma.

  16. Current state of our knowledge on brain tumor epidemiology.

    PubMed

    Ostrom, Quinn T; Barnholtz-Sloan, Jill S

    2011-06-01

    The overall incidence of brain tumors for benign and malignant tumors combined is 18.71 per 100,000 person-years; 11.52 per 100,000 person-years for benign tumors and 7.19 per 100,000 person-years for malignant tumors. Incidence, response to treatment, and survival after diagnosis vary greatly by age at diagnosis, histologic type of tumor, and degree of neurologic compromise. The only established environmental risk factor for brain tumors is ionizing radiation exposure. Exposure to radiofrequency electromagnetic fields via cell phone use has gained a lot of attention as a potential risk factor for brain tumor development. However, studies have been inconsistent and inconclusive due to systematic differences in study designs and difficulty of accurately measuring cell phone use. Recently studies of genetic risk factors for brain tumors have expanded to genome-wide association studies. In addition, genome-wide studies of somatic genetic changes in tumors show correlation with clinical outcomes.

  17. Chemo Drug May Combat Serious Brain Tumor After All

    MedlinePlus

    ... Chemo Drug May Combat Serious Brain Tumor After All Certain glioblastomas respond to anti-angiogenic compounds, study ... Dec. 22, 2016 HealthDay Copyright (c) 2016 HealthDay . All rights reserved. News stories are written and provided ...

  18. 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.

  19. 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 ...

  20. General Information about 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 ...

  1. 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

  2. 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

  3. Challenges for the functional diffusion map in pediatric brain tumors

    PubMed Central

    Grech-Sollars, Matthew; Saunders, Dawn E.; Phipps, Kim P.; Kaur, Ramneek; Paine, Simon M.L.; Jacques, Thomas S.; Clayden, Jonathan D.; Clark, Chris A.

    2014-01-01

    Background The functional diffusion map (fDM) has been suggested as a tool for early detection of tumor treatment efficacy. We aim to study 3 factors that could act as potential confounders in the fDM: areas of necrosis, tumor grade, and change in tumor size. Methods Thirty-four pediatric patients with brain tumors were enrolled in a retrospective study, approved by the local ethics committee, to examine the fDM. Tumors were selected to encompass a range of types and grades. A qualitative analysis was carried out to compare how fDM findings may be affected by each of the 3 confounders by comparing fDM findings to clinical image reports. Results Results show that the fDM in areas of necrosis do not discriminate between treatment response and tumor progression. Furthermore, tumor grade alters the behavior of the fDM: a decrease in apparent diffusion coefficient (ADC) is a sign of tumor progression in high-grade tumors and treatment response in low-grade tumors. Our results also suggest using only tumor area overlap between the 2 time points analyzed for the fDM in tumors of varying size. Conclusions Interpretation of fDM results needs to take into account the underlying biology of both tumor and healthy tissue. Careful interpretation of the results is required with due consideration to areas of necrosis, tumor grade, and change in tumor size. PMID:24305721

  4. Characterization of distinct immunophenotypes across pediatric brain tumor types.

    PubMed

    Griesinger, Andrea M; Birks, Diane K; Donson, Andrew M; Amani, Vladimir; Hoffman, Lindsey M; Waziri, Allen; Wang, Michael; Handler, Michael H; Foreman, Nicholas K

    2013-11-01

    Despite increasing evidence that antitumor immune control exists in the pediatric brain, these findings have yet to be exploited successfully in the clinic. A barrier to development of immunotherapeutic strategies in pediatric brain tumors is that the immunophenotype of these tumors' microenvironment has not been defined. To address this, the current study used multicolor FACS of disaggregated tumor to systematically characterize the frequency and phenotype of infiltrating immune cells in the most common pediatric brain tumor types. The initial study cohort consisted of 7 pilocytic astrocytoma (PA), 19 ependymoma (EPN), 5 glioblastoma (GBM), 6 medulloblastoma (MED), and 5 nontumor brain (NT) control samples obtained from epilepsy surgery. Immune cell types analyzed included both myeloid and T cell lineages and respective markers of activated or suppressed functional phenotypes. Immune parameters that distinguished each of the tumor types were identified. PA and EPN demonstrated significantly higher infiltrating myeloid and lymphoid cells compared with GBM, MED, or NT. Additionally, PA and EPN conveyed a comparatively activated/classically activated myeloid cell-skewed functional phenotype denoted in particular by HLA-DR and CD64 expression. In contrast, GBM and MED contained progressively fewer infiltrating leukocytes and more muted functional phenotypes similar to that of NT. These findings were recapitulated using whole tumor expression of corresponding immune marker genes in a large gene expression microarray cohort of pediatric brain tumors. The results of this cross-tumor comparative analysis demonstrate that different pediatric brain tumor types exhibit distinct immunophenotypes, implying that specific immunotherapeutic approaches may be most effective for each tumor type.

  5. Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration.

    PubMed

    Bai, Jordan; Trinh, Thi Lan Huong; Chuang, Kai-Hsiang; Qiu, Anqi

    2012-07-01

    Although many atlas-based segmentation methods have been developed and validated for the human brain, limited work has been done for the mouse brain. This paper investigated roles of image registration and segmentation model complexity in the mouse brain segmentation. We employed four segmentation models [single atlas, multiatlas, simultaneous truth and performance level estimation (STAPLE) and Markov random field (MRF) via four different image registration algorithms (affine, B-spline free-form deformation (FFD), Demons and large deformation diffeomorphic metric mapping (LDDMM)] for delineating 19 structures from in vivo magnetic resonance microscopy images. We validated their accuracies against manual segmentation. Our results revealed that LDDMM outperformed Demons, FFD and affine in any of the segmentation models. Under the same registration, increasing segmentation model complexity from single atlas to multiatlas, STAPLE or MRF significantly improved the segmentation accuracy. Interestingly, the multiatlas-based segmentation using nonlinear registrations (FFD, Demons and LDDMM) had similar performance to their STAPLE counterparts, while they both outperformed their MRF counterparts. Furthermore, when the single-atlas affine segmentation was used as reference, the improvement due to nonlinear registrations (FFD, Demons and LDDMM) in the single-atlas segmentation model was greater than that due to increasing model complexity (multiatlas, STAPLE and MRF affine segmentation). Hence, we concluded that image registration plays a more crucial role in the atlas-based automatic mouse brain segmentation as compared to model complexity. Multiple atlases with LDDMM can best improve the segmentation accuracy in the mouse brain among all segmentation models tested in this study.

  6. 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.

  7. 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

  8. Sports and childhood brain tumors: Can I play?

    PubMed Central

    Perreault, Sébastien; Lober, Robert M.; Davis, Carissa; Stave, Christopher; Partap, Sonia; Fisher, Paul G.

    2014-01-01

    Background It is unknown whether children with brain tumors have a higher risk of complications while participating in sports. We sought to estimate the prevalence of such events by conducting a systematic review of the literature, and we surveyed providers involved with pediatric central nervous system (CNS) tumor patients. Methods A systematic review of the literature in the PubMed, Scopus, and Cochrane databases was conducted for original articles addressing sport-related complications in the brain-tumor population. An online questionnaire was created to survey providers involved with pediatric CNS tumor patients about their current recommendations and experience regarding sports and brain tumors. Results We retrieved 32 subjects, including 19 pediatric cases from the literature. Most lesions associated with sport complications were arachnoid cysts (n = 21), followed by glioma (n = 5). The sports in which symptom onset most commonly occurred were soccer (n = 7), football (n = 5), and running (n = 5). We surveyed 111 pediatric neuro-oncology providers. Sport restriction varied greatly from none to 14 sports. Time to return to play in sports with contact also varied considerably between providers. Rationales for limiting sports activities were partly related to subspecialty. Responders reported 9 sport-related adverse events in patients with brain tumor. Conclusions Sport-related complications are uncommon in children with brain tumors. Patients might not be at a significantly higher risk and should not need to be excluded from most sports activities. PMID:26034627

  9. 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.

  10. 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.

  11. Current status of gene therapy for brain tumors.

    PubMed

    Murphy, Andrea M; Rabkin, Samuel D

    2013-04-01

    Glioblastoma (GBM) is the most common and deadliest primary brain tumor in adults, with current treatments having limited impact on disease progression. Therefore the development of alternative treatment options is greatly needed. Gene therapy is a treatment strategy that relies on the delivery of genetic material, usually transgenes or viruses, into cells for therapeutic purposes, and has been applied to GBM with increasing promise. We have included selectively replication-competent oncolytic viruses within this strategy, although the virus acts directly as a complex biologic anti-tumor agent rather than as a classic gene delivery vehicle. GBM is a good candidate for gene therapy because tumors remain locally within the brain and only rarely metastasize to other tissues; the majority of cells in the brain are post-mitotic, which allows for specific targeting of dividing tumor cells; and tumors can often be accessed neurosurgically for administration of therapy. Delivery vehicles used for brain tumors include nonreplicating viral vectors, normal adult stem/progenitor cells, and oncolytic viruses. The therapeutic transgenes or viruses are typically cytotoxic or express prodrug activating suicide genes to kill glioma cells, immunostimulatory to induce or amplify anti-tumor immune responses, and/or modify the tumor microenvironment such as blocking angiogenesis. This review describes current preclinical and clinical gene therapy strategies for the treatment of glioma.

  12. Current status of gene therapy for brain tumors

    PubMed Central

    MURPHY, ANDREA M.; RABKIN, SAMUEL D.

    2013-01-01

    Glioblastoma (GBM) is the most common and deadliest primary brain tumor in adults, with current treatments having limited impact on disease progression. Therefore the development of alternative treatment options is greatly needed. Gene therapy is a treatment strategy that relies on the delivery of genetic material, usually transgenes or viruses, into cells for therapeutic purposes, and has been applied to GBM with increasing promise. We have included selectively replication-competent oncolytic viruses within this strategy, although the virus acts directly as a complex biologic anti-tumor agent rather than as a classic gene delivery vehicle. GBM is a good candidate for gene therapy because tumors remain locally within the brain and only rarely metastasize to other tissues; the majority of cells in the brain are post-mitotic, which allows for specific targeting of dividing tumor cells; and tumors can often be accessed neurosurgically for administration of therapy. Delivery vehicles used for brain tumors include nonreplicating viral vectors, normal adult stem/progenitor cells, and oncolytic viruses. The therapeutic transgenes or viruses are typically cytotoxic or express prodrug activating suicide genes to kill glioma cells, immunostimulatory to induce or amplify anti-tumor immune responses, and/or modify the tumor microenvironment such as blocking angiogenesis. This review describes current preclinical and clinical gene therapy strategies for the treatment of glioma. PMID:23246627

  13. 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

  14. An epigenetic gateway to brain tumor cell identity.

    PubMed

    Mack, Stephen C; Hubert, Christopher G; Miller, Tyler E; Taylor, Michael D; Rich, Jeremy N

    2016-01-01

    Precise targeting of genetic lesions alone has been insufficient to extend brain tumor patient survival. Brain cancer cells are diverse in their genetic, metabolic and microenvironmental compositions, accounting for their phenotypic heterogeneity and disparate responses to therapy. These factors converge at the level of the epigenome, representing a unified node that can be disrupted by pharmacologic inhibition. Aberrant epigenomes define many childhood and adult brain cancers, as demonstrated by widespread changes to DNA methylation patterns, redistribution of histone marks and disruption of chromatin structure. In this Review, we describe the convergence of genetic, metabolic and microenvironmental factors on mechanisms of epigenetic deregulation in brain cancer. We discuss how aberrant epigenetic pathways identified in brain tumors affect cell identity, cell state and neoplastic transformation, as well as addressing the potential to exploit these alterations as new therapeutic strategies for the treatment of brain cancer.

  15. 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

  16. Morphology-driven automatic segmentation of MR images of the neonatal brain.

    PubMed

    Gui, Laura; Lisowski, Radoslaw; Faundez, Tamara; Hüppi, Petra S; Lazeyras, François; Kocher, Michel

    2012-12-01

    The segmentation of MR images of the neonatal brain is an essential step in the study and evaluation of infant brain development. State-of-the-art methods for adult brain MRI segmentation are not applicable to the neonatal brain, due to large differences in structure and tissue properties between newborn and adult brains. Existing newborn brain MRI segmentation methods either rely on manual interaction or require the use of atlases or templates, which unavoidably introduces a bias of the results towards the population that was used to derive the atlases. We propose a different approach for the segmentation of neonatal brain MRI, based on the infusion of high-level brain morphology knowledge, regarding relative tissue location, connectivity and structure. Our method does not require manual interaction, or the use of an atlas, and the generality of its priors makes it applicable to different neonatal populations, while avoiding atlas-related bias. The proposed algorithm segments the brain both globally (intracranial cavity, cerebellum, brainstem and the two hemispheres) and at tissue level (cortical and subcortical gray matter, myelinated and unmyelinated white matter, and cerebrospinal fluid). We validate our algorithm through visual inspection by medical experts, as well as by quantitative comparisons that demonstrate good agreement with expert manual segmentations. The algorithm's robustness is verified by testing on variable quality images acquired on different machines, and on subjects with variable anatomy (enlarged ventricles, preterm- vs. term-born).

  17. Tumor segmentation on FDG-PET: usefulness of locally connected conditional random fields

    NASA Astrophysics Data System (ADS)

    Nishio, Mizuho; Kono, Atsushi K.; Koyama, Hisanobu; Nishii, Tatsuya; Sugimura, Kazuro

    2015-03-01

    This study aimed to develop software for tumor segmentation on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). To segment the tumor from the background, we used graph cut, whose segmentation energy was generally divided into two terms: the unary and pairwise terms. Locally connected conditional random fields (LCRF) was proposed for the pairwise term. In LCRF, a three-dimensional cubic window with length L was set for each voxel, and voxels within the window were considered for the pairwise term. To evaluate our method, 64 clinically suspected metastatic bone tumors were tested, which were revealed by FDG-PET. To obtain ground truth, the tumors were manually delineated via consensus of two board-certified radiologists. To compare the LCRF accuracy, other types of segmentation were also applied such as region-growing based on 35%, 40%, and 45% of the tumor maximum standardized uptake value (RG35, RG40, and RG45, respectively), SLIC superpixels (SS), and region-based active contour models (AC). To validate the tumor segmentation accuracy, a dice similarity coefficient (DSC) was calculated between manual segmentation and result of each technique. The DSC difference was tested using the Wilcoxon signed rank test. The mean DSCs of LCRF at L = 3, 5, 7, and 9 were 0.784, 0.801, 0.809, and 0.812, respectively. The mean DSCs of other techniques were RG35, 0.633; RG40, 0.675; RG45, 0.689; SS, 0.709; and AC, 0.758. The DSC differences between LCRF and other techniques were statistically significant (p <0.05). In conclusion, tumor segmentation was more reliably performed with LCRF relative to other techniques.

  18. 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.

  19. 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.

  20. Doublecortin is preferentially expressed in invasive human brain tumors.

    PubMed

    Daou, Marie-Claire; Smith, Thomas W; Litofsky, N Scott; Hsieh, Chung C; Ross, Alonzo H

    2005-11-01

    Doublecortin (DCX) is required for neuroblastic migration during the development of the cerebral cortex. DCX is a microtubule-associated protein that plays a role in cellular motility. These facts led us to hypothesize that DCX is increased in invasive brain tumors. DCX expression was assessed in 69 paraffin-embedded brain tumors of neuroepithelial origin. In addition, mouse brain sections of the subventricular zone and dentate gyrus were used as positive controls for immunostaining, and specificity of antibody staining was demonstrated by peptide neutralization. DCX was highly expressed in both high-grade invasive tumors (glioblastoma, n=11; anaplastic astrocytoma/oligoastrocytoma, n=7; and medulloblastoma/PNET, n=6) and low-grade invasive tumors (oligodendroglioma, n=3; and astrocytoma/oligoastrocytoma, n=5). However, DCX was less intensely expressed in the circumscribed group of tumors (pilocytic astrocytoma, n=6; ependymoma/subependymoma, n=7; dysembryoplastic neuroepithelial tumor, n=4; ganglioglioma, n=2; meningioma, n=9; and schwannoma, n=9). By the Cochran-Mantel-Haenszel statistical test, the circumscribed group was significantly different from both the high-grade invasive group (P<0.0001) and the low-grade invasive group (P<0.0001). We conclude that DCX is preferentially expressed in invasive brain tumors. In addition, DCX immunostaining was stronger at the margin of the tumor than at the center. For a subset of these tumors, we also detected DCX mRNA and protein by Northern and Western blotting. DCX mRNA and protein was detected in glioma cell lines by Northern blotting, immunofluorescence microscopy and Western blotting. Collectively, the immunohistochemistry, Western blots and Northern blots conclusively demonstrate expression of DCX by human brain tumors.

  1. Gene therapeutics: the future of brain tumor therapy?

    PubMed

    Cutter, Jennifer L; Kurozumi, Kazuhiko; Chiocca, E Antonio; Kaur, Balveen

    2006-07-01

    Primary glioblastoma multiforme is an aggressive brain tumor that has no cure. Current treatments include gross resection of the tumor, radiation and chemotherapy. Despite valiant efforts, prognosis remains dismal. A promising new technique involves the use of oncolytic viruses that can specifically replicate and lyse in cancers, without spreading to normal tissues. Currently, these are being tested in relevant preclinical models and clinical trials as a therapeutic modality for many types of cancer. Results from recent clinical trials with oncolytic viruses have revealed the safety of this approach, although evidence for efficacy remains elusive. Oncolytic viral strategies are summarized in this review, with a focus on therapies used in brain tumors.

  2. 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

  3. 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

  4. Brain tissue segmentation in PET-CT images using probabilistic atlas and variational Bayes inference.

    PubMed

    Xia, Yong; Wang, Jiabin; Eberl, Stefan; Fulham, Michael; Feng, David Dagan

    2011-01-01

    PET-CT provides aligned anatomical (CT) and functional (PET) images in a single scan, and has the potential to improve brain PET image segmentation, which can in turn improve quantitative clinical analyses. We propose a statistical segmentation algorithm that incorporates the prior anatomical knowledge represented by probabilistic brain atlas into the variational Bayes inference to delineate gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in brain PET-CT images. Our approach adds an additional novel aspect by allowing voxels to have variable and adaptive prior probabilities of belonging to each class. We compared our algorithm to the segmentation approaches implemented in the expectation maximization segmentation (EMS) and statistical parametric mapping (SPM8) packages in 26 clinical cases. The results show that our algorithm improves the accuracy of brain PET-CT image segmentation.

  5. Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging.

    PubMed

    Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C

    2010-06-01

    We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation.

  6. 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.

  7. 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.

  8. [Surgery of metastatic brain tumors with new surgical instruments].

    PubMed

    Nomura, K; Shibui, S; Matsuoka, K; Watanabe, T; Nakamura, O

    1987-05-01

    The risk of damages of neurological function by the operation of metastatic brain tumors was reduced considerably after introduction of neurosurgical apparatuses, such as ultrasonograph, ultrasonic surgical aspirator and laser scalpel. Of these, ultrasonograph is useful to indicate the exact location of brain tumor at real time during the operation. Ultrasonic surgical aspirator reduced the risk of damage on important brain structures due to the selectivity of fragmentation and the safety of the dissection in the vicinity of important vessels and nerve tissues. Laser scalpel is also useful to extirpate the hemorrhagic tumor with hard consistency. Cases introduced in this paper were: case 1, brain metastasis from lung cancer located just under the left motor area in brain; case 2, metastasis with abundant neovascularization from renal cancer to orbital cavity which showed invasion to orbital roof and frontal bone; case 3, radiation induced sarcoma after the treatment of retinoblastoma; case 4, a large cerebellar metastatic tumor; case 5, neurogenic sarcoma which were successfully removed by using one of or combination of ultrasonograph, ultrasonic aspirator and laser scalpel. Advantage of these new instruments for the surgery on metastatic brain tumor was mentioned here. However, it is necessarily to get a custom before we use these apparatuses at operation efficiently.

  9. 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.

  10. 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.

  11. Critical Care Management of Cerebral Edema in Brain Tumors.

    PubMed

    Esquenazi, Yoshua; Lo, Victor P; Lee, Kiwon

    2017-01-01

    Cerebral edema associated with brain tumors is extremely common and can occur in both primary and metastatic tumors. The edema surrounding brain tumors results from leakage of plasma across the vessel wall into the parenchyma secondary to disruption of the blood-brain barrier. The clinical signs of brain tumor edema depend on the location of the tumor as well as the extent of the edema, which often exceeds the mass effect induced by the tumor itself. Uncontrolled cerebral edema may result in increased intracranial pressure and acute herniation syndromes that can result in permanent neurological dysfunction and potentially fatal herniation. Treatment strategies for elevated intracranial pressure consist of general measures, medical interventions, and surgery. Alhough the definitive treatment for the edema may ultimately be surgical resection of the tumor, the impact of the critical care management cannot be underestimated and thus patients must be vigilantly monitored in the intensive care unit. In this review, we discuss the pathology, pathophysiology, and clinical features of patients presenting with cerebral edema. Imaging findings and treatment modalities used in the intensive care unit are also discussed.

  12. 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

  13. 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

  14. Preoperative Coiling of Coexisting Intracranial Aneurysm and Subsequent Brain Tumor Surgery

    PubMed Central

    Park, Keun Young; Kim, Dong Joon

    2016-01-01

    Objective Few studies have investigated treatment strategies for brain tumor with a coexisting unruptured intracranial aneurysm (cUIA). The purpose of this study was to evaluate the safety and efficacy of preoperative coiling for cUIA, and subsequent brain tumor surgery. Materials and Methods A total of 19 patients (mean age, 55.2 years; M:F = 4:15) underwent preoperative coiling for 23 cUIAs and subsequent brain tumor surgery. Primary brain tumors were meningiomas (n = 7, 36.8%), pituitary adenomas (n = 7, 36.8%), gliomas (n = 3, 15.8%), vestibular schwannoma (n = 1, 5.3%), and Rathke's cleft cyst (n = 1, 5.3%). cUIAs were located at the distal internal carotid artery (n = 9, 39.1%), anterior cerebral artery (n = 8, 34.8%), middle cerebral artery (n = 4, 17.4%), basilar artery top (n = 1, 4.3%), and posterior cerebral artery, P1 segment (n = 1, 4.3%). The outcomes of preoperative coiling of cUIA and subsequent brain tumor surgery were retrospectively evaluated. Results Single-microcatheter technique was used in 13 cases (56.5%), balloon-assisted in 4 cases (17.4%), double-microcatheter in 4 cases (17.4%), and stent-assisted in 2 cases (8.7%). Complete cUIA occlusion was achieved in 18 cases (78.3%), while residual neck occurred in 5 cases (21.7%). The only coiling-related complication was 1 transient ischemic attack (5.3%). Neurological deterioration did not occur in any patient during the period between coiling and tumor surgery. At the latest clinical follow-up (mean, 29 months; range, 2–120 months), 15 patients (78.9%) had favorable outcomes (modified Rankin Scale, 0–2), while 4 patients (21.1%) had unfavorable outcomes due to consequences of brain tumor surgery. Conclusion Preoperative coiling and subsequent tumor surgery was safe and effective, making it a reasonable treatment option for patients with brain tumor and cUIA. PMID:27833409

  15. 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.

  16. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM.

    PubMed

    Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

    2017-01-01

    The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

  17. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

    PubMed Central

    Ray, Arun Kumar; Thethi, Har Pal

    2017-01-01

    The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques. PMID:28367213

  18. 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

  19. A Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation

    PubMed Central

    Wang, Hongzhi; Das, Sandhitsu R.; Suh, Jung Wook; Altinay, Murat; Pluta, John; Craige, Caryne; Avants, Brian; Yushkevich, Paul A.

    2011-01-01

    We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter

  20. Exosomes as Tools to Suppress Primary Brain Tumor.

    PubMed

    Katakowski, Mark; Chopp, Michael

    2016-04-01

    Exosomes are small microvesicles released by cells that efficiently transfer their molecular cargo to other cells, including tumor. Exosomes may pass the blood-brain barrier and have been demonstrated to deliver RNAs contained within to brain. As they are non-viable, the risk profile of exosomes is thought to be less than that of cellular therapies. Exosomes can be manufactured at scale in culture, and exosomes can be engineered to incorporate therapeutic miRNAs, siRNAs, or chemotherapeutic molecules. As natural biological delivery vehicles, interest in the use of exosomes as therapeutic delivery agents is growing. We previously demonstrated a novel treatment whereby mesenchymal stromal cells were employed to package tumor-suppressing miR-146b into exosomes, which were then used to reduce malignant glioma growth in rat. The use of exosomes to raise the immune system against tumor is also drawing interest. Exosomes from dendritic cells which are antigen-presenting, and have been used for treatment of brain tumor may be divided into three categories: (1) exosomes for immunomodulation-based therapy, (2) exosomes as delivery vehicles for anti-tumor nucleotides, and (3) exosomes as drug delivery vehicles. Here, we will provide an overview of these three applications of exosomes to treat brain tumor, and examine their prospects on the long road to clinical use.

  1. Telomerase activity in human brain tumors: astrocytoma and meningioma.

    PubMed

    Kheirollahi, Majid; Mehrazin, Masoud; Kamalian, Naser; Mohammadi-asl, Javad; Mehdipour, Parvin

    2013-05-01

    Somatic cells do not have telomerase activity but immortalized cell lines and more than 85 % of the cancer cells show telomerase activation to prevent the telomere from progressive shortening. The activation of this enzyme has been found in a variety of human tumors and tumor-derived cell lines, but only few studies on telomerase activity in human brain tumors have been reported. Here, we evaluated telomerase activity in different grades of human astrocytoma and meningioma brain tumors. In this study, assay for telomerase activity performed on 50 eligible cases consisted of 26 meningioma, 24 astrocytoma according to the standard protocols. In the brain tissues, telomerase activity was positive in 39 (65 %) of 50 patients. One sample t test showed that the telomerase activity in meningioma and astrocytoma tumors was significantly positive entirely (P < 0.001). Also, grade I of meningioma and low grades of astrocytoma (grades I and II) significantly showed telomerase activity. According to our results, we suggest that activation of telomerase is an event that starts mostly at low grades of brain including meningioma and astrocytoma tumors.

  2. Factors affecting the cerebral network in brain tumor patients.

    PubMed

    Heimans, Jan J; Reijneveld, Jaap C

    2012-06-01

    Brain functions, including cognitive functions, are frequently disturbed in brain tumor patients. These disturbances may result from the tumor itself, but also from the treatment directed against the tumor. Surgery, radiotherapy and chemotherapy all may affect cerebral functioning, both in a positive as well as in a negative way. Apart from the anti-tumor treatment, glioma patients often receive glucocorticoids and anti-epileptic drugs, which both also have influence on brain functioning. The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole. Any network, whether it be a neural, a social or an electronic network, can be described in parameters assessing the topological characteristics of that particular network. Repeated assessment of neural network characteristics in brain tumor patients during their disease course enables study of the dynamics of neural networks and provides more insight into the plasticity of the diseased brain. Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering". Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures. Future research should be focused on both plasticity of neural networks and the factors that have impact on that plasticity as well as the possible role of assessment of neural network characteristics in the determination of cerebral function during the disease course.

  3. Brain mapping in tumors: intraoperative or extraoperative?

    PubMed

    Duffau, Hugues

    2013-12-01

    In nontumoral epilepsy surgery, the main goal for all preoperative investigation is to first determine the epileptogenic zone, and then to analyze its relation to eloquent cortex, in order to control seizures while avoiding adverse postoperative neurologic outcome. To this end, in addition to neuropsychological assessment, functional neuroimaging and scalp electroencephalography, extraoperative recording, and electrical mapping, especially using subdural strip- or grid-electrodes, has been reported extensively. Nonetheless, in tumoral epilepsy surgery, the rationale is different. Indeed, the first aim is rather to maximize the extent of tumor resection while minimizing postsurgical morbidity, in order to increase the median survival as well as to preserve quality of life. As a consequence, as frequently seen in infiltrating tumors such as gliomas, where these lesions not only grow but also migrate along white matter tracts, the resection should be performed according to functional boundaries both at cortical and subcortical levels. With this in mind, extraoperative mapping by strips/grids is often not sufficient in tumoral surgery, since in essence, it allows study of the cortex but cannot map subcortical pathways. Therefore, intraoperative electrostimulation mapping, especially in awake patients, is more appropriate in tumor surgery, because this technique allows real-time detection of areas crucial for cerebral functions--eloquent cortex and fibers--throughout the resection. In summary, rather than choosing one or the other of different mapping techniques, methodology should be adapted to each pathology, that is, extraoperative mapping in nontumoral epilepsy surgery and intraoperative mapping in tumoral surgery.

  4. Application of SLT contact laser in resection of brain tumors

    NASA Astrophysics Data System (ADS)

    Li, Han-Jie; Li, Zhi-Qiang; Li, Chan-Yuan

    1998-11-01

    28 cases of brain tumors were operated by SLT contact Nd:YAG laser from October 1995 to May 1997 in our hospital. Among these, 14 are menin-giomas, 5 are astrocytomas. Others are tumors such as acoustic neuromas, craniopharyngiomas, etc 21 cases underwent common craniotomy, 3, laser endoscopy operation; and 4, laser therapy under microscopy. Method of tumor resection: firstly, cutting and separating the tumor from brain tissues with GRP by 5-15w; secondly, vaporizing parenchyma of tumor with MTRL and sucking it, again, cutting and separating and so on, lastly removing the tumor entirely. The power of vaporization for glioma or tumors in ventricles is about 20-30w, but for meningiomas, 30-60w. MT was used on power of 15-20w to coagulate and homeostate the left cavity of tumor. According to our experience, laser operation can make bleeding reduced markedly, tumor resection become more thorough, and postoperative response and complications decrease obviously.

  5. 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.

  6. 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.

  7. Growth inhibition, tumor maturation, and extended survival in experimental brain tumors in rats treated with phenylacetate.

    PubMed

    Ram, Z; Samid, D; Walbridge, S; Oshiro, E M; Viola, J J; Tao-Cheng, J H; Shack, S; Thibault, A; Myers, C E; Oldfield, E H

    1994-06-01

    Phenylacetate is a naturally occurring plasma component that suppresses the growth of tumor cells and induces differentiation in vitro. To evaluate the in vivo potential and preventive and therapeutic antitumor efficacy of sodium phenylacetate against malignant brain tumors, Fischer 344 rats (n = 50) bearing cerebral 9L gliosarcomas received phenylacetate by continuous s.c. release starting on the day of tumor inoculation (n = 10) using s.c. osmotic minipumps (550 mg/kg/day for 28 days). Rats with established brain tumors (n = 12) received continuous s.c. phenylacetate supplemented with additional daily i.p. dose (300 mg/kg). Control rats (n = 25) were treated in a similar way with saline. Rats were sacrificed during treatment for electron microscopic studies of their tumors, in vivo proliferation assays, and measurement of phenylacetate levels in the serum and cerebrospinal fluid. Treatment with phenylacetate extended survival when started on the day of tumor inoculation (P < 0.01) or 7 days after inoculation (P < 0.03) without any associated adverse effects. In the latter group, phenylacetate levels in pooled serum and cerebrospinal fluid samples after 7 days of treatment were in the therapeutic range as determined in vitro (2.45 mM in serum and 3.1 mM in cerebrospinal fluid). Electron microscopy of treated tumors demonstrated marked hypertrophy and organization of the rough endoplasmic reticulum, indicating cell differentiation, in contrast to the scant and randomly distributed endoplasmic reticulum in tumors from untreated animals. In addition, in vitro studies demonstrated dose-dependent inhibition of the rate of tumor proliferation and restoration of anchorage dependency, a marker of phenotypic reversion. Phenylacetate, used at clinically achievable concentrations, prolongs survival of rats with malignant brain tumors through induction of tumor differentiation. Its role in the treatment of brain tumors and other cancers should be explored further.

  8. Culture and Isolation of Brain Tumor Initiating Cells.

    PubMed

    Vora, Parvez; Venugopal, Chitra; McFarlane, Nicole; Singh, Sheila K

    2015-08-03

    Brain tumors are typically composed of heterogeneous cells that exhibit distinct phenotypic characteristics and proliferative potentials. Only a relatively small fraction of cells in the tumor with stem cell properties, termed brain tumor initiating cells (BTICs), possess an ability to differentiate along multiple lineages, self-renew, and initiate tumors in vivo. This unit describes protocols for the culture and isolation BTICs. We applied culture conditions and assays originally used for normal neural stem cells (NSCs) in vitro to a variety of brain tumors. Using fluorescence-activated cell sorting for the neural precursor cell surface marker CD133/CD15, BTICs can be isolated and studied prospectively. Isolation of BTICs from GBM bulk tumor will enable examination of dissimilar morphologies, self-renewal capacities, tumorigenicity, and therapeutic sensitivities. As cancer is also considered a disease of unregulated self-renewal and differentiation, an understanding of BTICs is fundamental to understanding tumor growth. Ultimately, it will lead to novel drug discovery approaches that strategically target the functionally relevant BTIC population.

  9. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.

    PubMed

    Tohka, Jussi

    2014-11-28

    Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

  10. Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans.

    PubMed

    Freeborough, P A; Fox, N C; Kitney, R I

    1997-05-01

    Interactive algorithms are an attractive approach to the accurate segmentation of 3D brain scans as they potentially improve the reliability of fully automated segmentation while avoiding the labour intensiveness and inaccuracies of manual segmentation. We present a 3D image analysis package (MIDAS) with a novel architecture enabling highly interactive segmentation algorithms to be implemented as add on modules. Interactive methods based on intensity thresholding, region growing and the constrained application of morphological operators are also presented. The methods involve the application of constraints and freedoms on the algorithms coupled with real time visualisation of the effect. This methodology has been applied to the segmentation, visualisation and measurement of the whole brain and a small irregular neuroanatomical structure, the hippocampus. We demonstrate reproducible and anatomically accurate segmentations of these structures. The efficacy of one method in measuring volume loss (atrophy) of the hippocampus in Alzheimer's disease is shown and is compared to conventional methods.

  11. 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.

  12. Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

    PubMed Central

    Guo, Yu; Feng, Yuanming; Sun, Jian; 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. PMID:24987451

  13. 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.

  14. Delayed Contrast Extravasation MRI for Depicting Tumor and Non-Tumoral Tissues in Primary and Metastatic Brain Tumors

    PubMed Central

    Zach, Leor; Guez, David; Last, David; Daniels, Dianne; Grober, Yuval; Nissim, Ouzi; Hoffmann, Chen; Nass, Dvora; Talianski, Alisa; Spiegelmann, Roberto; Cohen, Zvi R.; Mardor, Yael

    2012-01-01

    The current standard of care for newly diagnosed glioblastoma multiforme (GBM) is resection followed by radiotherapy with concomitant and adjuvant temozolomide. Recent studies suggest that nearly half of the patients with early radiological deterioration post treatment do not suffer from tumor recurrence but from pseudoprogression. Similarly, a significant number of patients with brain metastases suffer from radiation necrosis following radiation treatments. Conventional MRI is currently unable to differentiate tumor progression from treatment-induced effects. The ability to clearly differentiate tumor from non-tumoral tissues is crucial for appropriate patient management. Ten patients with primary brain tumors and 10 patients with brain metastases were scanned by delayed contrast extravasation MRI prior to surgery. Enhancement subtraction maps calculated from high resolution MR images acquired up to 75 min after contrast administration were used for obtaining stereotactic biopsies. Histological assessment was then compared with the pre-surgical calculated maps. In addition, the application of our maps for prediction of progression was studied in a small cohort of 13 newly diagnosed GBM patients undergoing standard chemoradiation and followed up to 19.7 months post therapy. The maps showed two primary enhancement populations: the slow population where contrast clearance from the tissue was slower than contrast accumulation and the fast population where clearance was faster than accumulation. Comparison with histology confirmed the fast population to consist of morphologically active tumor and the slow population to consist of non-tumoral tissues. Our maps demonstrated significant correlation with perfusion-weighted MR data acquired simultaneously, although contradicting examples were shown. Preliminary results suggest that early changes in the fast volumes may serve as a predictor for time to progression. These preliminary results suggest that our high resolution

  15. A fast atlas pre-selection procedure for multi-atlas based brain segmentation.

    PubMed

    Ma, Jingbo; Ma, Heather T; Li, Hengtong; Ye, Chenfei; Wu, Dan; Tang, Xiaoying; Miller, Michael; Mori, Susumu

    2015-01-01

    Multi-atlas based MR image segmentation has been recognized as a quantitative analysis approach for brain. For such purpose, atlas databases keep increasing to include various anatomical characteristics of human brain. Atlas pre-selection becomes a necessary step for efficient and accurate automated segmentation of human brain images. In this study, we proposed a method of atlas pre-selection for target image segmentation on the MriCloud platform, which is a state-of-the-art multi-atlas based segmentation tool. In the MRIcloud pipeline, segmentation of lateral ventricle (LV) label is generated as an additional input in the segmentation pipeline. Under this circumstance, similarity of the LV label between target image and atlases was adopted as the atlas ranking scheme. Dice overlap coefficient was calculated and taken as the quantitative measure for atlas ranking. Segmentation results based on the proposed method were compared with that based on atlas pre-selection by mutual information (MI) between images. The final segmentation results showed a comparable accuracy of the proposed method with that from MI based atlas pre-selection. However, the computation load for the atlas pre-selection was speeded up by about 20 times compared to MI based pre-selection. The proposed method provides a promising assistance for quantitative analysis of brain images.

  16. Remote Postoperative Epidural Hematoma after Brain Tumor Surgery

    PubMed Central

    Chung, Ho-Jung; Park, Jae-Sung; Jeun, Sin-Soo

    2015-01-01

    A postoperative epidural hematoma (EDH) is a serious and embarrassing complication, which usually occurs at the site of operation after intracranial surgery. However, remote EDH is relatively rare. We report three cases of remote EDH after brain tumor surgery. All three cases seemed to have different causes of remote postoperative EDH; however, all patients were managed promptly and showed excellent outcomes. Although the exact mechanism of remote postoperative EDH is unknown, surgeons should be cautious of the speed of lowering intracranial pressure and implement basic procedures to prevent this hazardous complication of brain tumor surgery. PMID:26605271

  17. Interpreting WAIS-III performance after primary brain tumor surgery.

    PubMed

    Gonçalves, Marta de A; Simões, Mário R; Castro-Caldas, Alexandre

    2017-01-01

    The literature lacks information on the performance of patients with brain tumors on the Wechsler Intelligence Scales. This study aimed to explore the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) performance profile of 23 consecutive patients with brain tumors and 23 matched controls selected from the Portuguese WAIS-III standardization sample, using the technical manual steps recommended for score interpretation. The control group was demographically matched to the tumor group regarding gender, age, education, profession, and geographic region. The technical manual steps recommended for score interpretation were applied. Patients with brain tumors had significantly lower performances on the Performance IQ, Full-Scale IQ, Perceptual Organization Index, Working Memory Index, Processing Speed Index, Arithmetic, Object Assembly, and Picture Arrangement, though all scaled scores were within the normal range according to the manual tables. Only Vocabulary and Comprehension scatter scores were statistically different between groups. No strengths or weaknesses were found for either group. The mean discrepancy scores do not appear to have clinical value for this population. In conclusion, the study results did not reveal a specific profile for patients with brain tumors on the WAIS-III.

  18. Training stem cells for treatment of malignant brain tumors.

    PubMed

    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

    2014-09-26

    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.

  19. Diffusion in the extracellular space in brain and tumors

    NASA Astrophysics Data System (ADS)

    Verkman, A. S.

    2013-08-01

    Diffusion of solutes and macromolecules in the extracellular space (ECS) in brain is important for non-synaptic intercellular communication, extracellular ionic buffering, and delivery of drugs and metabolites. Diffusion in tumor ECS is important for delivery of anti-tumor drugs. The ECS in brain comprises ˜20% of brain parenchymal volume and contains cell-cell gaps down to ˜50 nm. We have developed fluorescence methods to quantify solute diffusion in the ECS, allowing measurements deep in solid tissues using microfiberoptics with micron tip size. Diffusion through the tortuous ECS in brain is generally slowed by ˜3-5-fold compared with that in water, with approximately half of the slowing due to tortuous ECS geometry and half due to the mildly viscous extracellular matrix (ECM). Mathematical modeling of slowed diffusion in an ECS with reasonable anatomical accuracy is in good agreement with experiment. In tumor tissue, diffusion of small macromolecules is only mildly slowed (<3-fold slower than in water) in superficial tumor, but is greatly slowed (>10-fold) at a depth of few millimeters as the tumor tissue becomes more compact. Slowing by ECM components such as collagen contribute to the slowed diffusion. Therefore, as found within cells, cellular crowding and highly tortuous transport can produce only minor slowing of diffusion in the ECS.

  20. 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

  1. 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.

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

    PubMed

    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.

  3. 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

  4. Three-dimensional segmentation of the tumor mass in computed tomographic images of neuroblastoma

    NASA Astrophysics Data System (ADS)

    Deglint, Hanford J.; Rangayyan, Rangaraj M.; Boag, Graham S.

    2004-05-01

    Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, fibrosis, and normal tissue are often intermixed. Rather than attempt to separate these tissue types into distinct regions, we propose to explore methods to delineate the normal structures expected in abdominal CT images, remove them from further consideration, and examine the remaining parts of the images for the tumor mass. We explore the use of fuzzy connectivity for this purpose. Expert knowledge provided by the radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are also incorporated in the segmentation algorithm. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to chemotherapy and in the planning of delayed surgery for resection of the tumor. The performance of the algorithm is evaluated using cases acquired from the Alberta Children's Hospital.

  5. 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.

  6. A chance-constrained programming level set method for longitudinal segmentation of lung tumors in CT.

    PubMed

    Rouchdy, Youssef; Bloch, Isabelle

    2011-01-01

    This paper presents a novel stochastic level set method for the longitudinal tracking of lung tumors in computed tomography (CT). The proposed model addresses the limitations of registration based and segmentation based methods for longitudinal tumor tracking. It combines the advantages of each approach using a new probabilistic framework, namely Chance-Constrained Programming (CCP). Lung tumors can shrink or grow over time, which can be reflected in large changes of shape, appearance and volume in CT images. Traditional level set methods with a priori knowledge about shape are not suitable since the tumors are undergoing random and large changes in shape. Our CCP level set model allows to introduce a flexible prior to track structures with a highly variable shape by permitting a constraint violation of the prior up to a specified probability level. The chance constraints are computed from two given points by the user or from segmented tumors from a reference image. The reference image can be one of the images studied or an external template. We present a numerical scheme to approximate the solution of the proposed model and apply it to track lung tumors in CT. Finally, we compare our approach with a Bayesian level set. The CCP level set model gives the best results: it is more coherent with the manual segmentation.

  7. 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.

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

    PubMed Central

    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.

    2015-01-01

    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. PMID:26575352

  9. Intracranial foreign body granuloma simulating brain tumor: a case report

    PubMed Central

    Saeidiborojeni, Hamid Reza; Fakheri, Taravat; Iizadi, Babak

    2011-01-01

    Intracranial foreign body granulomas are rarely reported. Clinical symptoms caused by foreign body granulomas can be noticed from months to many years after surgical procedure. The most common reported etiology is suture material. A 45-year-old woman was presented with grand mal epilepsy. She was operated for brain tumor 19 years ago. In CT scan, a round radio-dense mass resembling a tumor at anterior fossa was seen. She underwent craniotomy and resected a granuloma with cotton fibers surrounded by yellow capsule without residual or recurrent tumor. Granuloma can mimic intracranial meningioma and special attention should be paid not to leave cotton pledgets during operations. PMID:22091258

  10. 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

  11. 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.

  12. Dexamethasone alleviates tumor-associated brain damage and angiogenesis.

    PubMed

    Fan, Zheng; Sehm, Tina; Rauh, Manfred; Buchfelder, Michael; Eyupoglu, Ilker Y; Savaskan, Nicolai E

    2014-01-01

    Children and adults with the most aggressive form of brain cancer, malignant gliomas or glioblastoma, often develop cerebral edema as a life-threatening complication. This complication is routinely treated with dexamethasone (DEXA), a steroidal anti-inflammatory drug with pleiotropic action profile. Here we show that dexamethasone reduces murine and rodent glioma tumor growth in a concentration-dependent manner. Low concentrations of DEXA are already capable of inhibiting glioma cell proliferation and at higher levels induce cell death. Further, the expression of the glutamate antiporter xCT (system Xc-; SLC7a11) and VEGFA is up-regulated after DEXA treatment indicating early cellular stress responses. However, in human gliomas DEXA exerts differential cytotoxic effects, with some human glioma cells (U251, T98G) resistant to DEXA, a finding corroborated by clinical data of dexamethasone non-responders. Moreover, DEXA-resistant gliomas did not show any xCT alterations, indicating that these gene expressions are associated with DEXA-induced cellular stress. Hence, siRNA-mediated xCT knockdown in glioma cells increased the susceptibility to DEXA. Interestingly, cell viability of primary human astrocytes and primary rodent neurons is not affected by DEXA. We further tested the pharmacological effects of DEXA on brain tissue and showed that DEXA reduces tumor-induced disturbances of the microenvironment such as neuronal cell death and tumor-induced angiogenesis. In conclusion, we demonstrate that DEXA inhibits glioma cell growth in a concentration and species-dependent manner. Further, DEXA executes neuroprotective effects in brains and reduces tumor-induced angiogenesis. Thus, our investigations reveal that DEXA acts pleiotropically and impacts tumor growth, tumor vasculature and tumor-associated brain damage.

  13. Dexamethasone Alleviates Tumor-Associated Brain Damage and Angiogenesis

    PubMed Central

    Fan, Zheng; Sehm, Tina; Rauh, Manfred; Buchfelder, Michael

    2014-01-01

    Children and adults with the most aggressive form of brain cancer, malignant gliomas or glioblastoma, often develop cerebral edema as a life-threatening complication. This complication is routinely treated with dexamethasone (DEXA), a steroidal anti-inflammatory drug with pleiotropic action profile. Here we show that dexamethasone reduces murine and rodent glioma tumor growth in a concentration-dependent manner. Low concentrations of DEXA are already capable of inhibiting glioma cell proliferation and at higher levels induce cell death. Further, the expression of the glutamate antiporter xCT (system Xc−; SLC7a11) and VEGFA is up-regulated after DEXA treatment indicating early cellular stress responses. However, in human gliomas DEXA exerts differential cytotoxic effects, with some human glioma cells (U251, T98G) resistant to DEXA, a finding corroborated by clinical data of dexamethasone non-responders. Moreover, DEXA-resistant gliomas did not show any xCT alterations, indicating that these gene expressions are associated with DEXA-induced cellular stress. Hence, siRNA-mediated xCT knockdown in glioma cells increased the susceptibility to DEXA. Interestingly, cell viability of primary human astrocytes and primary rodent neurons is not affected by DEXA. We further tested the pharmacological effects of DEXA on brain tissue and showed that DEXA reduces tumor-induced disturbances of the microenvironment such as neuronal cell death and tumor-induced angiogenesis. In conclusion, we demonstrate that DEXA inhibits glioma cell growth in a concentration and species-dependent manner. Further, DEXA executes neuroprotective effects in brains and reduces tumor-induced angiogenesis. Thus, our investigations reveal that DEXA acts pleiotropically and impacts tumor growth, tumor vasculature and tumor-associated brain damage. PMID:24714627

  14. 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

  15. MRI virtual biopsy and treatment of brain metastatic tumors with targeted nanobioconjugates: nanoclinic in the brain.

    PubMed

    Patil, Rameshwar; Ljubimov, Alexander V; Gangalum, Pallavi R; Ding, Hui; Portilla-Arias, Jose; Wagner, Shawn; Inoue, Satoshi; Konda, Bindu; Rekechenetskiy, Arthur; Chesnokova, Alexandra; Markman, Janet L; Ljubimov, Vladimir A; Li, Debiao; Prasad, Ravi S; Black, Keith L; Holler, Eggehard; Ljubimova, Julia Y

    2015-05-26

    Differential diagnosis of brain magnetic resonance imaging (MRI) enhancement(s) remains a significant problem, which may be difficult to resolve without biopsy, which can be often dangerous or even impossible. Such MRI enhancement(s) can result from metastasis of primary tumors such as lung or breast, radiation necrosis, infections, or a new primary brain tumor (glioma, meningioma). Neurological symptoms are often the same on initial presentation. To develop a more precise noninvasive MRI diagnostic method, we have engineered a new class of poly(β-l-malic acid) polymeric nanoimaging agents (NIAs). The NIAs carrying attached MRI tracer are able to pass through the blood-brain barrier (BBB) and specifically target cancer cells for efficient imaging. A qualitative/quantitative "MRI virtual biopsy" method is based on a nanoconjugate carrying MRI contrast agent gadolinium-DOTA and antibodies recognizing tumor-specific markers and extravasating through the BBB. In newly developed double tumor xenogeneic mouse models of brain metastasis this noninvasive method allowed differential diagnosis of HER2- and EGFR-expressing brain tumors. After MRI diagnosis, breast and lung cancer brain metastases were successfully treated with similar tumor-targeted nanoconjugates carrying molecular inhibitors of EGFR or HER2 instead of imaging contrast agent. The treatment resulted in a significant increase in animal survival and markedly reduced immunostaining for several cancer stem cell markers. Novel NIAs could be useful for brain diagnostic MRI in the clinic without currently performed brain biopsies. This technology shows promise for differential MRI diagnosis and treatment of brain metastases and other pathologies when biopsies are difficult to perform.

  16. Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures.

    PubMed

    Powell, Stephanie; Magnotta, Vincent A; Johnson, Hans; Jammalamadaka, Vamsi K; Pierson, Ronald; Andreasen, Nancy C

    2008-01-01

    The large amount of imaging data collected in several ongoing multi-center studies requires automated methods to delineate brain structures of interest. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures. Here we present several automated segmentation methods using multidimensional registration. A direct comparison between template, probability, artificial neural network (ANN) and support vector machine (SVM)-based automated segmentation methods is presented. Three metrics for each segmentation method are reported in the delineation of subcortical and cerebellar brain regions. Results show that the machine learning methods outperform the template and probability-based methods. Utilization of these automated segmentation methods may be as reliable as manual raters and require no rater intervention.

  17. 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…

  18. Life Satisfaction in Adult Survivors of Childhood Brain Tumors

    PubMed Central

    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, life-long 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 suggests 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 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. PMID:25027187

  19. 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.

  20. 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.

  1. 3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

    PubMed Central

    Yezzi, Anthony; Cohen, Laurent D.

    2006-01-01

    Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods. PMID:23165037

  2. Gene therapy for brain tumors: basic developments and clinical implementation.

    PubMed

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

    2012-10-11

    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.

  3. 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

  4. Antiangiogenic (metronomic) chemotherapy for brain tumors: current and future perspectives.

    PubMed

    Samuel, David P; Wen, Patrick Y; Kieran, Mark W

    2009-07-01

    Significant advances in the diagnosis and treatment of brain tumors have been made through better imaging, surgical techniques and advances in radiation therapy. However, the cure rate for most adult and pediatric brain tumor patients has not mirrored this success. Angiogenesis, the development of neovascularization, provides the required nutrients and oxygen to an expanding tumor and is controlled by a complex balance of proangiogenic cytokines and antiangiogenic factors. A series of new inhibitors of angiogenesis are now in clinical trials. Most of these rely on inhibiting tumor cell-mediated cytokines or blocking the activation of their cognate receptors. Cytotoxic chemotherapy, by contrast, targets dividing cells but can be modulated to attack dividing endothelial cells. This review will focus on the use of low-dose antiangiogenic (also called metronomic) chemotherapy to inhibit endothelial cell function and resultant neovascularization in the treatment of adult and pediatric brain tumors. By examining the biology and preclinical findings that led to the development of antiangiogenic/metronomic chemotherapy, clinical studies have been undertaken that support the role of this approach in the clinic, and have led to the introduction of a number of markers being used to better predict active combinations and appropriate patient populations.

  5. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

    PubMed

    Gholipour, Ali; Rollins, Caitlin K; Velasco-Annis, Clemente; Ouaalam, Abdelhakim; Akhondi-Asl, Alireza; Afacan, Onur; Ortinau, Cynthia M; Clancy, Sean; Limperopoulos, Catherine; Yang, Edward; Estroff, Judy A; Warfield, Simon K

    2017-03-28

    Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.

  6. 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.

  7. Effect of blood vessel segmentation on the outcome of electroporation-based treatments of liver tumors.

    PubMed

    Marčan, Marija; Kos, Bor; Miklavčič, Damijan

    2015-01-01

    Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses applied to tissue via electrodes. To ensure that the whole tumor is covered with sufficiently high electric field, accurate numerical models are built based on individual patient anatomy. Extraction of patient's anatomy through segmentation of medical images inevitably produces some errors. In order to ensure the robustness of treatment planning, it is necessary to evaluate the potential effect of such errors on the electric field distribution. In this work we focus on determining the effect of errors in automatic segmentation of hepatic vessels on the electric field distribution in electroporation-based treatments in the liver. First, a numerical analysis was performed on a simple 'sphere and cylinder' model for tumors and vessels of different sizes and relative positions. Second, an analysis of two models extracted from medical images of real patients in which we introduced variations of an error of the automatic vessel segmentation method was performed. The results obtained from a simple model indicate that ignoring the vessels when calculating the electric field distribution can cause insufficient coverage of the tumor with electric fields. Results of this study indicate that this effect happens for small (10 mm) and medium-sized (30 mm) tumors, especially in the absence of a central electrode inserted in the tumor. The results obtained from the real-case models also show higher negative impact of automatic vessel segmentation errors on the electric field distribution when the central electrode is absent. However, the average error of the automatic vessel segmentation did not have an impact on the electric field distribution if the central electrode was present. This suggests the algorithm is robust enough to be used in creating a model for treatment parameter optimization, but with a central electrode.

  8. 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.

  9. Alterations of telomere length in human brain tumors.

    PubMed

    Kheirollahi, Majid; Mehrazin, Masoud; Kamalian, Naser; Mehdipour, Parvin

    2011-09-01

    Telomeres at the ends of human chromosomes consist of tandem hexametric (TTAGGG)n repeats, which protect them from degradation. At each cycle of cell division, most normal somatic cells lose approximately 50-100 bp of the terminal telomeric repeat DNA. Precise prediction of growth and estimation of the malignant potential of brain tumors require additional markers. DNA extraction was performed from the 51 frozen tissues, and a non-radioactive chemiluminescent assay was used for Southern blotting. One sample t-test shows highly significant difference in telomere length in meningioma and astrocytoma with normal range. According to our results, higher grades of meningioma and astrocytoma tumors show more heterogeneity in telomere length, and also it seems shortening process of telomeres is an early event in brain tumors.

  10. Effects of penetrating traumatic brain injury on event segmentation and memory.

    PubMed

    Zacks, Jeffrey M; Kurby, Christopher A; Landazabal, Claudia S; Krueger, Frank; Grafman, Jordan

    2016-01-01

    Penetrating traumatic brain injury (pTBI) is associated with deficits in cognitive tasks including comprehension and memory, and also with impairments in tasks of daily living. In naturalistic settings, one important component of cognitive task performance is event segmentation, the ability to parse the ongoing stream of behavior into meaningful units. Event segmentation ability is associated with memory performance and with action control, but is not well assessed by standard neuropsychological assessments or laboratory tasks. Here, we measured event segmentation and memory in a sample of 123 male military veterans aged 59-81 who had suffered a traumatic brain injury as young men, and 34 demographically similar controls. Participants watched movies of everyday activities and segmented them to identify fine-grained or coarse-grained events, and then completed tests of recognition memory for pictures from the movies and of memory for the temporal order of actions in the movies. Lesion location and volume were assessed with computed tomography (CT) imaging. Patients with traumatic brain injury were impaired on event segmentation. Those with larger lesions had larger impairments for fine segmentation and also impairments for both memory measures. Further, the degree of memory impairment was statistically mediated by the degree of event segmentation impairment. There was some evidence that lesions to the ventromedial prefrontal cortex (vmPFC) selectively impaired coarse segmentation; however, lesions outside of a priori regions of interest also were associated with impaired segmentation. One possibility is that the effect of vmPFC damage reflects the role of prefrontal event knowledge representations in ongoing comprehension. These results suggest that assessment of naturalistic event comprehension can be a valuable component of cognitive assessment in cases of traumatic brain injury, and that interventions aimed at event segmentation could be clinically helpful.

  11. Effects of Penetrating Traumatic Brain Injury on Event Segmentation and Memory

    PubMed Central

    Zacks, Jeffrey M.; Kurby, Christopher A.; Landazabal, Claudia S.; Krueger, Frank; Grafman, Jordan

    2015-01-01

    Penetrating traumatic brain injury is associated with deficits in cognitive tasks including comprehension and memory, and also with impairments in tasks of daily living. In naturalistic settings, one important component of cognitive task performance is event segmentation, the ability to parse the ongoing stream of behavior into meaningful units. Event segmentation ability is associated with memory performance and with action control, but is not well assessed by standard neuropsychological assessments or laboratory tasks. Here, we measured event segmentation and memory in a sample of 123 male military veterans aged 59–81 who had suffered a traumatic brain injury as young men, and 34 demographically similar controls. Participants watched movies of everyday activities and segmented them to identify fine-grained or coarse-grained events, and then completed tests of recognition memory for pictures from the movies and of memory for the temporal order of actions in the movies. Lesion location and volume were assessed with computed tomography imaging. Patients with traumatic brain injury were impaired on event segmentation. Those with larger lesions had larger impairments for fine segmentation and also impairments for both memory measures. Further, the degree of memory impairment was statistically mediated by the degree of event segmentation impairment. There was some evidence that lesions to the ventromedial prefrontal cortex (vmPFC) selectively impaired coarse segmentation; however, lesions outside of a priori regions of interest also were associated with impaired segmentation. One possibility is that the effect of vmPFC damage reflects the role of prefrontal event knowledge representations in ongoing comprehension. These results suggest that assessment of naturalistic event comprehension can be a valuable component of cognitive assessment in cases of traumatic brain injury, and that interventions aimed at event segmentation could be clinically helpful. PMID:26704077

  12. 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

  13. 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

  14. Simulation of brain tumor resection in image-guided neurosurgery

    NASA Astrophysics Data System (ADS)

    Fan, Xiaoyao; Ji, Songbai; Fontaine, Kathryn; Hartov, Alex; Roberts, David; Paulsen, Keith

    2011-03-01

    Preoperative magnetic resonance images are typically used for neuronavigation in image-guided neurosurgery. However, intraoperative brain deformation (e.g., as a result of gravitation, loss of cerebrospinal fluid, retraction, resection, etc.) significantly degrades the accuracy in image guidance, and must be compensated for in order to maintain sufficient accuracy for navigation. Biomechanical finite element models are effective techniques that assimilate intraoperative data and compute whole-brain deformation from which to generate model-updated MR images (uMR) to improve accuracy in intraoperative guidance. To date, most studies have focused on early surgical stages (i.e., after craniotomy and durotomy), whereas simulation of more complex events at later surgical stages has remained to be a challenge using biomechanical models. We have developed a method to simulate partial or complete tumor resection that incorporates intraoperative volumetric ultrasound (US) and stereovision (SV), and the resulting whole-brain deformation was used to generate uMR. The 3D ultrasound and stereovision systems are complimentary to each other because they capture features deeper in the brain beneath the craniotomy and at the exposed cortical surface, respectively. In this paper, we illustrate the application of the proposed method to simulate brain tumor resection at three temporally distinct surgical stages throughout a clinical surgery case using sparse displacement data obtained from both the US and SV systems. We demonstrate that our technique is feasible to produce uMR that agrees well with intraoperative US and SV images after dural opening, after partial tumor resection, and after complete tumor resection. Currently, the computational cost to simulate tumor resection can be up to 30 min because of the need for re-meshing and the trial-and-error approach to refine the amount of tissue resection. However, this approach introduces minimal interruption to the surgical workflow

  15. Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

    PubMed Central

    Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L.; Rueckert, Daniel; Thiran, Jean-Philippe

    2017-01-01

    Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. PMID:28350816

  16. 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.

  17. What Are the Risk Factors for Brain and Spinal Cord Tumors in Children?

    MedlinePlus

    ... and Prevention What Are the Risk Factors for Brain and Spinal Cord Tumors in Children? A risk ... Factors with uncertain, controversial, or unproven effects on brain tumor risk Cell phone use Cell phones give ...

  18. Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

    PubMed Central

    Ra, Young-Shin

    2017-01-01

    Conventional anatomic brain MRI is often limited in evaluating pediatric brain tumors, the most common solid tumors and a leading cause of death in children. Advanced brain MRI techniques have great potential to improve diagnostic performance in children with brain tumors and overcome diagnostic pitfalls resulting from diverse tumor pathologies as well as nonspecific or overlapped imaging findings. Advanced MRI techniques used for evaluating pediatric brain tumors include diffusion-weighted imaging, diffusion tensor imaging, functional MRI, perfusion imaging, spectroscopy, susceptibility-weighted imaging, and chemical exchange saturation transfer imaging. Because pediatric brain tumors differ from adult counterparts in various aspects, MRI protocols should be designed to achieve maximal clinical benefits in pediatric brain tumors. In this study, we review advanced MRI techniques and interpretation algorithms for pediatric brain tumors. PMID:28096729

  19. Imaging of brain tumor proliferative activity with iodine-131-iododeoxyuridine

    SciTech Connect

    Tjuvajev, J.G.; Macapinlac, H.A.; Daghighian, F.

    1994-09-01

    Iodine-131-iododeoxyuridine (IUdR) uptake and retention was imaged with SPECT at 2 and 24 hr after administering a 10-mCi dose to six patients with primary brain tumors. The SPECT images were directly compared to gadolinium contrast-enhanced MR images as well as to ({sup 18}F) fluorodeoxyglucose (FDG) PET scans and {sup 201}Tl SPECT scans. Localized uptake and retention of IUdR-derived radioactivity was observed in five of six patients. The plasma half-life of ({sup 131}I) IUdR was short (1.5 min) in comparison to the half-life of total plasma radioactivity (6.4 hr). The pattern of ({sup 131}I)IUdR-derived radioactivity was markedly different in the 2-hr compared to 24-hr images. Radioactivity was localized along the periphery of the tumor and extended beyond the margin of tumor identified by contrast enhancement on MRI. The estimated levels of tumor radioactivity at 24 hr, based on semiquantitative phantom studies, ranged between <0.1 and 0.2 {mu}Ci/cc (<0.001% and 0.002% dose/cc); brain levels were not measurable. Iodine-131-IUdR SPECT imaging of brain tumor proliferation has low (marginal) sensitivity due to low count rates and can detect only the most active regions of tumor growth. Imaging at 24 hr represents a washout strategy to reduce {sup 131}I-labeled metabolites contributing to background activity in the tumors, and is more likely to show the pattern of ({sup 131}I)IUdR-DNA incorporation and thereby increase image specificity. Iodine-123-IUdR SPECT imaging at 12 hr and the use of ({sup 124}I)IUdR and PET will improve count acquisitions and image quality. 74 refs., 6 figs., 2 tabs.

  20. 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.

  1. 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.

  2. Segmentation of tumor ultrasound image in HIFU therapy based on texture and boundary encoding

    NASA Astrophysics Data System (ADS)

    Zhang, Dong; Xu, Menglong; Quan, Long; Yang, Yan; Qin, Qianqing; Zhu, Wenbin

    2015-02-01

    It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy. The approach oversegments the ultrasound image into some small regions, which are merged by using the principle of minimum description length (MDL) afterwards. Small regions belonging to the same tumor are clustered as they preserve similar texture features. The mergence is completed by obtaining the shortest coding length from encoding textures and boundaries of these regions in the clustering process. The tumor region is finally selected from merged regions by a proposed algorithm without manual interaction. The performance of the method is tested on 50 uterine fibroid ultrasound images from HIFU guiding transducers. The segmentations are compared with manual delineations to verify its feasibility. The quantitative evaluation with HIFU images shows that the mean true positive of the approach is 93.53%, the mean false positive is 4.06%, the mean similarity is 89.92%, the mean norm Hausdorff distance is 3.62% and the mean norm maximum average distance is 0.57%. The experiments validate that the proposed method can achieve favorable segmentation without manual initialization and effectively handle the poor quality of the ultrasound guidance image in HIFU therapy, which indicates that the approach is applicable in HIFU therapy.

  3. Emerging techniques and technologies in brain tumor imaging

    PubMed Central

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

    2014-01-01

    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 23Na 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. PMID:25313234

  4. Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

    PubMed Central

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-01-01

    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 health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

  5. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

    PubMed

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-03-01

    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 health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.

  6. Postictal Magnetic Resonance Imaging Changes Masquerading as Brain Tumor Progression: A Case Series

    PubMed Central

    Dunn-Pirio, Anastasie M.; Billakota, Santoshi; Peters, Katherine B.

    2016-01-01

    Seizures are common among patients with brain tumors. Transient, postictal magnetic resonance imaging abnormalities are a long recognized phenomenon. However, these radiographic changes are not as well studied in the brain tumor population. Moreover, reversible neuroimaging abnormalities following seizure activity may be misinterpreted for tumor progression and could consequently result in unnecessary tumor-directed treatment. Here, we describe two cases of patients with brain tumors who developed peri-ictal pseudoprogression and review the relevant literature. PMID:27462237

  7. An active contour model for medical image segmentation with application to brain CT image

    PubMed Central

    Qian, Xiaohua; Wang, Jiahui; Guo, Shuxu; Li, Qiang

    2013-01-01

    Purpose: Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images. Methods: The energy function of the region-based active contour model is composed of a range domain kernel function, a space domain kernel function, and an edge indicator function. By minimizing the energy function, the region of edge elements of the target could be automatically identified in images with less dependence on initial contours. The energy function was optimized by means of the deepest descent method with a level set framework. An overlap rate between segmentation results and the reference standard was used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on both synthetic data and real brain CT images. They also compared the performance level of our method to those of region-scalable fitting (RSF) and global convex segment (GCS) models. Results: For the experiment of CSF segmentation in 67 brain CT images, their method achieved an average overlap rate of 66% compared to the average overlap rates of 16% and 46% from the RSF model and the GCS model, respectively. Conclusions: Their region-based active contour model has the ability to achieve accurate segmentation results in images with high noise level and intensity inhomogeneity. Therefore, their method has great potential in the segmentation of medical images and would be useful for developing CAD schemes for acute ischemic stroke in brain CT images. PMID:23387759

  8. 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.

  9. 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.

  10. An accurate and efficient bayesian method for automatic segmentation of brain MRI.

    PubMed

    Marroquin, J L; Vemuri, B C; Botello, S; Calderon, F; Fernandez-Bouzas, A

    2002-08-01

    Automatic three-dimensional (3-D) segmentation of the brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Of the techniques reported in the literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3-D segmentation procedure for brain MR scans. It has several salient features; namely, the following. 1) Instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. 2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from nonbrain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. 3) Finally, a novel algorithm is presented which is a variant of the expectation-maximization procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.

  11. Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm.

    PubMed

    Zygomalas, Apollon; Karavias, Dionissios; Koutsouris, Dimitrios; Maroulis, Ioannis; Karavias, Dimitrios D; Giokas, Konstantinos; Megalooikonomou, Vasileios

    2016-05-01

    We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance.

  12. History and evolution of brain tumor imaging: insights through radiology.

    PubMed

    Castillo, Mauricio

    2014-11-01

    This review recounts the history of brain tumor diagnosis from antiquity to the present and, indirectly, the history of neuroradiology. Imaging of the brain has from the beginning held an enormous interest because of the inherent difficulty of this endeavor due to the presence of the skull. Because of this, most techniques when newly developed have always been used in neuroradiology and, although some have proved to be inappropriate for this purpose, many were easily incorporated into the specialty. The first major advance in modern neuroimaging was contrast agent-enhanced computed tomography, which permitted accurate anatomic localization of brain tumors and, by virtue of contrast enhancement, malignant ones. The most important advances in neuroimaging occurred with the development of magnetic resonance imaging and diffusion-weighted sequences that allowed an indirect estimation of tumor cellularity; this was further refined by the development of perfusion and permeability mapping. From its beginnings with indirect and purely anatomic imaging techniques, neuroradiology now uses a combination of anatomic and physiologic techniques that will play a critical role in biologic tumor imaging and radiologic genomics.

  13. Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.

    PubMed

    Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.

  14. 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.

  15. 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

  16. Impact of Markov Random Field Optimizer on MRI-based Tissue Segmentation in the Aging Brain

    PubMed Central

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

    2013-01-01

    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 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. Comparisons among Graph Cuts (GC), Belief Propagation (BP), and Iterative Conditional Modes (ICM) suggested that in the elderly brain, BP and GC provide the highest segmentation performance, with a slight advantage to BP, and that performance is often superior to that provided by popular methods SPM and FAST. Conversely, SPM and FAST excelled in the young brain, thus emphasizing the unique challenges involved in imaging the aging brain. PMID:22256150

  17. 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.

  18. Neurodegeneration in the Brain Tumor Microenvironment: Glutamate in the Limelight

    PubMed Central

    Savaskan, Nicolai E.; Fan, Zheng; Broggini, Thomas; Buchfelder, Michael; Eyüpoglu, Ilker Y.

    2015-01-01

    Malignant brain tumors are characterized by destructive growth and neuronal cell death making them one of the most devastating diseases. Neurodegenerative actions of malignant gliomas resemble mechanisms also found in many neurodegenerative diseases such as Alzheimer's and Parkinson's diseases and amyotrophic lateral sclerosis. Recent data demonstrate that gliomas seize neuronal glutamate signaling for their own growth advantage. Excessive glutamate release via the glutamate/cystine antiporter xCT (system xc-, SLC7a11) renders cancer cells resistant to chemotherapeutics and create the tumor microenvironment toxic for neurons. In particular the glutamate/cystine antiporter xCT takes center stage in neurodegenerative processes and sets this transporter a potential prime target for cancer therapy. Noteworthy is the finding, that reactive oxygen species (ROS) activate transient receptor potential (TRP) channels and thereby TRP channels can potentiate glutamate release. Yet another important biological feature of the xCT/glutamate system is its modulatory effect on the tumor microenvironment with impact on host cells and the cancer stem cell niche. The EMA and FDA-approved drug sulfasalazine (SAS) presents a lead compound for xCT inhibition, although so far clinical trials on glioblastomas with SAS were ambiguous. Here, we critically analyze the mechanisms of action of xCT antiporter on malignant gliomas and in the tumor microenvironment. Deciphering the impact of xCT and glutamate and its relation to TRP channels in brain tumors pave the way for developing important cancer microenvironmental modulators and drugable lead targets. PMID:26411769

  19. 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.

  20. Advanced MR Imaging in Pediatric Brain Tumors, Clinical Applications.

    PubMed

    Lequin, Maarten; Hendrikse, Jeroen

    2017-02-01

    Advanced MR imaging techniques, such as spectroscopy, perfusion, diffusion, and functional imaging, have improved the diagnosis of brain tumors in children and also play an important role in defining surgical as well as therapeutic responses in these patients. In addition to the anatomic or structural information gained with conventional MR imaging sequences, advanced MR imaging techniques also provide physiologic information about tumor morphology, metabolism, and hemodynamics. This article reviews the physiology, techniques, and clinical applications of diffusion-weighted and diffusion tensor imaging, MR spectroscopy, perfusion MR imaging, susceptibility-weighted imaging, and functional MR imaging in the setting of neuro-oncology.

  1. Collecting and Storing Blood and Brain Tumor Tissue Samples From Children With Brain Tumors

    ClinicalTrials.gov

    2016-11-21

    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

  2. Cellular phones and risk of brain tumors.

    PubMed

    Frumkin, H; Jacobson, A; Gansler, T; Thun, M J

    2001-01-01

    As cellular telephones are a relatively new technology, we do not yet have long-term follow-up on their possible biological effects. However, the lack of ionizing radiation and the low energy level emitted from cell phones and absorbed by human tissues make it unlikely that these devices cause cancer. Moreover, several well-designed epidemiologic studies find no consistent association between cell phone use and brain cancer. It is impossible to prove that any product or exposure is absolutely safe, especially in the absence of very long-term follow-up. Accordingly, the following summary from the Food and Drug Administration Center for Devices and Radiological Health offers advice to people concerned about their risk: If there is a risk from these products--and at this point we do not know that there is--it is probably very small. But if people are concerned about avoiding even potential risks, there are simple steps they can take to do so. People who must conduct extended conversations in their cars every day could switch to a type of mobile phone that places more distance between their bodies and the source of the RF, since the exposure level drops off dramatically with distance. For example, they could switch to: a mobile phone in which the antenna is located outside the vehicle, a hand-held phone with a built-in antenna connected to a different antenna mounted on the outside of the car or built into a separate package, or a headset with a remote antenna to a mobile phone carried at the waist. Again the scientific data do not demonstrate that mobile phones are harmful. But if people are concerned about the radiofrequency energy from these products, taking the simple precautions outlined above can reduce any possible risk. In addition, people who are concerned might choose digital rather than analog telephones, since the former use lower RF levels.

  3. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients

    PubMed Central

    Pohl, Kilian M.; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M.; Kikinis, Ron; Black, Peter M.

    2011-01-01

    Background Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. Objective This article describes a semi-automatic monitoring approach using longitudinal medical images. We test the method on brain scans of meningioma patients, which experts found difficult to monitor as the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. Methods We describe a semi-automatic procedure targeted towards identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than five minutes, returns the total volume of tumor change in mm3. We test the method on post-gadolinium, T1-weighted Magnetic Resonance Images of ten meningioma patients and compare our results to experts’ findings. We also perform benchmark testing with synthetic data. Results Our experiments indicated that experts’ visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts’ manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts’ results. However, our approach required far less user input and generated more consistent measurements. Conclusion The sensitivity of experts’ visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts’ segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input. PMID:21206318

  4. Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors

    PubMed Central

    Ortiz, Andrés; Palacio, Antonio A.; Górriz, Juan M.; Ramírez, Javier; Salas-González, Diego

    2013-01-01

    Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR). PMID:23762192

  5. 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.

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

    PubMed

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

    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.

  7. 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. PMID:27668065

  8. Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

    PubMed

    Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

    2014-09-01

    Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.

  9. Late sequelae in children treated for brain tumors and leukemia.

    PubMed

    Jereb, B; Korenjak, R; Krzisnik, C; Petric-Grabnar, G; Zadravec-Zaletel, L; Anzic, J; Stare, J

    1994-01-01

    Forty-two survivors treated at an age of 2-16 years for brain tumors or leukemia were, 4-21 years after treatment, subjected to an extensive follow-up investigation, including physical examination and interview; 35 of them also had endocrinological and 33 psychological evaluation. Hormonal deficiencies were found in about two-thirds of patients and were most common in those treated for brain tumors. The great majority had verbal intelligence quotient (VIQ) within normal range. Also, the performance intelligence quotients (PIQ) were normal in most patients. However, the results suggested that the primary intellectual capacity in children treated for cancer was not being fully utilized, their PIQ being on the average higher than their VIQ; this tendency was especially pronounced in the leukemia patients.

  10. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy.

    PubMed

    Aljabar, P; Heckemann, R A; Hammers, A; Hajnal, J V; Rueckert, D

    2009-07-01

    Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

  11. Joint Brain Parametric T1-Map Segmentation and RF Inhomogeneity Calibration

    PubMed Central

    Chen, Ping-Feng; Steen, R. Grant; Yezzi, Anthony; Krim, Hamid

    2009-01-01

    We propose a constrained version of Mumford and Shah's (1989) segmentation model with an information-theoretic point of view in order to devise a systematic procedure to segment brain magnetic resonance imaging (MRI) data for parametric T1-Map and T1-weighted images, in both 2-D and 3D settings. Incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the 3-tissue segmentation possible. Moreover, we proposed a novel method to jointly segment the T1-Map and calibrate RF Inhomogeneity (JSRIC). This method assumes the average T1 value of white matter is the same across transverse slices in the central brain region, and JSRIC is able to rectify the flip angles to generate calibrated T1-Maps. In order to generate an accurate T1-Map, the determination of optimal flip-angles and the registration of flip-angle images are examined. Our JSRIC method is validated on two human subjects in the 2D T1-Map modality and our segmentation method is validated by two public databases, BrainWeb and IBSR, of T1-weighted modality in the 3D setting. PMID:19710938

  12. 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).

  13. 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

  14. Management of children with brain tumors in Paraguay

    PubMed Central

    Baskin, Jacquelyn L.; Lezcano, Eva; Kim, Bo Sung; Figueredo, Diego; Lassaletta, Alvaro; Perez-Martinez, Antonio; Madero, Luis; Caniza, Miguela A.; Howard, Scott C.; Samudio, Angelica; Finlay, Jonathan L.

    2013-01-01

    Background Cure rates among children with brain tumors differ between low-income and high-income countries. To evaluate causes of these differences, we analyzed aspects of care provided to pediatric neuro-oncology patients in a low middle-income South American country. Methods Three methods were used to evaluate treatment of children with brain tumors in Paraguay: (1) a quantitative needs assessment questionnaire for local treating physicians, (2) site visits to assess 3 tertiary care centers in Asunción and a satellite clinic in an underdeveloped area, and (3) interviews with health care workers from relevant disciplines to determine their perceptions of available resources. Treatment failure was defined as abandonment of therapy, relapse, or death. Results All 3 tertiary care facilities have access to chemotherapy and pediatric oncologists but lack training and tools for neuropathology and optimal neurosurgery. The 2 public hospitals also lack access to appropriate radiological tests and timely radiotherapy. These results demonstrate disparities in Paraguay, with rates of treatment failure ranging from 37% to 83% among the 3 facilities. Conclusions National and center-specific deficiencies in resources to manage pediatric brain tumors contribute to poor outcomes in Paraguay and suggest that both national and center-specific interventions are warranted to improve care. Disparities in Paraguay reflect different levels of governmental and philanthropic support, program development, and socio-economic status of patients and families, which must be considered when developing targeted strategies to improve management. Effective targeted interventions can serve as a model to develop pediatric brain tumor programs in other low- and middle-income countries. PMID:23197688

  15. Handling of solid brain tumor tissue for protein analysis.

    PubMed

    Ericsson, Christer; Nistér, Monica

    2011-01-01

    Optimal protein analysis requires unfixed tissue samples. We suggest handling the brain tumor tissue sterilely and coldly (on ice) for as short time as possible prior to processing, but for no more than 8 h. This simple protocol results in apparently intact morphology, immunoreactivity, protein integrity, and protein phosphorylation with the criteria we apply. Sample handling for Pathological Anatomical Diagnosis (PAD) and for protein analysis can be one and the same.

  16. 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.

  17. 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.

  18. A Patient-Specific Segmentation Framework for Longitudinal MR Images of Traumatic Brain Injury

    PubMed Central

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

    2013-01-01

    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. PMID:24465115

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. Gene markers in brain tumors: what the epileptologist should know.

    PubMed

    Ostrom, Quinn; Cohen, Mark L; Ondracek, Annie; Sloan, Andrew; Barnholtz-Sloan, Jill

    2013-12-01

    Gene markers or biomarkers can be used for diagnostic or prognostic purposes for all different types of complex disease, including brain tumors. Prognostic markers can be useful to explain differences not only in overall survival but also in response to treatment and for development of targeted therapies. Multiple genes with specific types of alterations have now been identified that are associated with improved response to chemotherapy and radiotherapy, such as O(6)-methylguanine methyltranferase (MGMT) or loss of chromosomes 1p and/or 19q. Other alterations have been identified that are associated with improved overall survival, such as mutations in isocitrate dehydrogenase 1 (IDH1) and/or isocitrate dehydrogenase 2 (IDH2) or having the glioma CpG island DNA methylator phenotype (G-CIMP). There are many biomarkers that may have relevance in brain tumor-associated epilepsy that do not respond to treatment. Given the rapidly changing landscape of high throughput "omics" technologies, there is significant potential for gaining further knowledge via integration of multiple different types of high genome-wide data. This knowledge can be translated into improved therapies and clinical outcomes for patients with brain tumors.

  4. Childhood brain tumors and paternal occupation in the aerospace industry.

    PubMed

    Olshan, A F; Breslow, N E; Daling, J R; Weiss, N S; Leviton, A

    1986-07-01

    Data from a case-control study of childhood brain tumors were analyzed to examine the possibility that paternal occupation in the aerospace industry is related to the development of a brain tumor in offspring. Parents of 51 children with brain tumors diagnosed in western Washington State during 1978-81 were interviewed, and their responses were compared to those of parents of 142 children selected at random from this population. Among all children, proportions of case and control fathers who had ever been employed in the aerospace industry were nearly identical [relative risk (RR) = 0.94; 95% confidence interval (CI) = 0.40-2.19]. Employment in the aerospace industry during the period from 1 year prior to birth to the time of diagnosis and any employment in the manufacturing part of the industry were not associated with increased risk. However, stratification by age at diagnosis revealed an increased risk associated with father's ever-employment in the industry (RR = 2.10; 95% CI = 0.79-5.60) for children under 10 years old. A corresponding decreased risk (RR = 0.12; 95% CI = 0.01-1.08) was found for children over 10 years old. Because of the relatively small number of cases with a positive paternal occupational history, interpretations of the difference in the direction of the association according to age at diagnosis must remain tentative ones.

  5. A Spatio-Temporal Atlas of the Human Fetal Brain with Application to Tissue Segmentation

    PubMed Central

    Habas, Piotr A.; Kim, Kio; Rousseau, Francois; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

    2012-01-01

    Modeling and analysis of MR images of the early developing human brain is a challenge because of the transient nature of different tissue classes during brain growth. To address this issue, a statistical model that can capture the spatial variation of structures over time is needed. Here, we present an approach to building a spatio-temporal model of tissue distribution in the developing brain which can incorporate both developed tissues as well as transient tissue classes such as the germinal matrix by using constrained higher order polynomial models. This spatio-temporal model is created from a set of manual segmentations through groupwise registration and voxelwise non-linear modeling of tissue class membership, that allows us to represent the appearance as well as disappearance of the transient brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific tissue probability maps and use them to initialize an EM segmentation of the fetal brain tissues. The approach is evaluated using clinical MR images of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Results indicate improvement in performance of atlas-based EM segmentation provided by higher order temporal models that capture the variation of tissue occurrence over time. PMID:20425999

  6. SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

    PubMed Central

    Serag, Ahmed; Wilkinson, Alastair G.; Telford, Emma J.; Pataky, Rozalia; Sparrow, Sarah A.; Anblagan, Devasuda; Macnaught, Gillian; Semple, Scott I.; Boardman, James P.

    2017-01-01

    Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course. PMID:28163680

  7. DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

    PubMed

    Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

    2016-12-08

    In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

  8. 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.

  9. Graph cut based co-segmentation of lung tumor in PET-CT images

    NASA Astrophysics Data System (ADS)

    Ju, Wei; Xiang, Dehui; Zhang, Bin; Chen, Xinjian

    2015-03-01

    Accurate segmentation of pulmonary tumor is important for clinicians to make appropriate diagnosis and treatment. Positron Emission Tomography (PET) and Computed Tomography (CT) are two commonly used imaging technologies for image-guided radiation therapy. In this study, we present a graph-based method to integrate the two modalities to segment the tumor simultaneously on PET and CT images. The co-segmentation problem is formulated as an energy minimization problem. Two weighted sub-graphs are constructed for PET and CT. The characteristic information of the two modalities is encoded on the edges of the graph. A context cost is enforced by adding context arcs to achieve consistent results between the two modalities. An optimal solution can be achieved by solving a maximum flow problem. The proposed segmentation method was validated on 18 sets of PET-CT images from different patients with non-small cell lung cancer (NSCLC). The quantitative results show significant improvement of our method with a mean DSC value 0.82.

  10. Research on the lesion segmentation of breast tumor MR images based on FCM-DS theory

    NASA Astrophysics Data System (ADS)

    Zhang, Liangbin; Ma, Wenjun; Shen, Xing; Li, Yuehua; Zhu, Yuemin; Chen, Li; Zhang, Su

    2017-03-01

    Magnetic resonance imaging (MRI) plays an important role in the treatment of breast tumor by high intensity focused ultrasound (HIFU). The doctors evaluate the scale, distribution and the statement of benign or malignancy of breast tumor by analyzing variety modalities of MRI, such as the T2, DWI and DCE images for making accurate preoperative treatment plan and evaluating the effect of the operation. This paper presents a method of lesion segmentation of breast tumor based on FCM-DS theory. Fuzzy c-means clustering (FCM) algorithm combined with Dempster-Shafer (DS) theory is used to process the uncertainty of information, segmenting the lesion areas on DWI and DCE modalities of MRI and reducing the scale of the uncertain parts. Experiment results show that FCM-DS can fuse the DWI and DCE images to achieve accurate segmentation and display the statement of benign or malignancy of lesion area by Time-Intensity Curve (TIC), which could be beneficial in making preoperative treatment plan and evaluating the effect of the therapy.

  11. 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.

  12. 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.

  13. 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

  14. Automated localization and segmentation of lung tumor from PET-CT thorax volumes based on image feature analysis.

    PubMed

    Cui, Hui; Wang, Xiuying; Feng, Dagan

    2012-01-01

    Positron emission tomography - computed tomography (PET-CT) plays an essential role in early tumor detection, diagnosis, staging and treatment. Automated and more accurate lung tumor detection and delineation from PET-CT is challenging. In this paper, on the basis of quantitative analysis of contrast feature of PET volume in SUV (standardized uptake value), our method firstly automatically localized the lung tumor. Then based on analysing the surrounding CT features of the initial tumor definition, our decision strategy determines the tumor segmentation from CT or from PET. The algorithm has been validated on 20 PET-CT studies involving non-small cell lung cancer (NSCLC). Experimental results demonstrated that our method was able to segment the tumor when adjacent to mediastinum or chest wall, and the algorithm outperformed the other five lung segmentation methods in terms of overlapping measure.

  15. Hierarchical segmentation-assisted multimodal registration for MR brain images.

    PubMed

    Lu, Huanxiang; Beisteiner, Roland; Nolte, Lutz-Peter; Reyes, Mauricio

    2013-04-01

    Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

  16. Tumor Metabolism, the Ketogenic Diet and β-Hydroxybutyrate: Novel Approaches to Adjuvant Brain Tumor Therapy

    PubMed Central

    Woolf, Eric C.; Syed, Nelofer; Scheck, Adrienne C.

    2016-01-01

    Malignant brain tumors are devastating despite aggressive treatments such as surgical resection, chemotherapy and radiation therapy. The average life expectancy of patients with newly diagnosed glioblastoma is approximately ~18 months. It is clear that increased survival of brain tumor patients requires the design of new therapeutic modalities, especially those that enhance currently available treatments and/or limit tumor growth. One novel therapeutic arena is the metabolic dysregulation that results in an increased need for glucose in tumor cells. This phenomenon suggests that a reduction in tumor growth could be achieved by decreasing glucose availability, which can be accomplished through pharmacological means or through the use of a high-fat, low-carbohydrate ketogenic diet (KD). The KD, as the name implies, also provides increased blood ketones to support the energy needs of normal tissues. Preclinical work from a number of laboratories has shown that the KD does indeed reduce tumor growth in vivo. In addition, the KD has been shown to reduce angiogenesis, inflammation, peri-tumoral edema, migration and invasion. Furthermore, this diet can enhance the activity of radiation and chemotherapy in a mouse model of glioma, thus increasing survival. Additional studies in vitro have indicated that increasing ketones such as β-hydroxybutyrate (βHB) in the absence of glucose reduction can also inhibit cell growth and potentiate the effects of chemotherapy and radiation. Thus, while we are only beginning to understand the pluripotent mechanisms through which the KD affects tumor growth and response to conventional therapies, the emerging data provide strong support for the use of a KD in the treatment of malignant gliomas. This has led to a limited number of clinical trials investigating the use of a KD in patients with primary and recurrent glioma. PMID:27899882

  17. 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.

  18. Automatic brain segmentation and validation: image-based versus atlas-based deformable models

    NASA Astrophysics Data System (ADS)

    Aboutanos, Georges B.; Dawant, Benoit M.

    1997-04-01

    Due to the complexity of the brain surface, there is at present no segmentation method that proves to work automatically and consistently on any 3-D magnetic resonance (MR) images of the head. There is a definite lack of validation studies related to automatic brain extraction. In this work we present an image-base automatic method for brain segmentation and use its results as an input to a deformable model method which we call image-based deformable model. Combining image-based methods with a deformable model can lead to a robust segmentation method without requiring registration of the image volumes into a standardized space, the automation of which remains challenging for pathological cases. We validate our segmentation results on 3-D MP-RAGE (magnetization-prepared rapid gradient-echo) volumes for the image model prior- and post-deformation and compare it to an atlas model prior- and post-deformation. Our validation is based on volume measurement comparison to manually segmented data. Our analysis shows that the improvement afforded by the deformable model methods are statistically significant, however there are no significant differences between the image-based and atlas-based deformable model methods.

  19. Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment.

    PubMed

    Kotrotsou, Aikaterini; Zinn, Pascal O; Colen, Rivka R

    2016-11-01

    The role of radiomics in the diagnosis, monitoring, and therapy planning of brain tumors is becoming increasingly clear. Incorporation of quantitative approaches in radiology, in combination with increased computer power, offers unique insights into macroscopic tumor characteristics and their direct association with the underlying pathophysiology. This article presents the most recent findings in radiomics and radiogenomics with respect to identifying potential imaging biomarkers with prognostic value that can lead to individualized therapy. In addition, a brief introduction to the concept of big data and its significance in medicine is presented.

  20. Automatic segmentation of tumor-laden lung volumes from the LIDC database

    NASA Astrophysics Data System (ADS)

    O'Dell, Walter G.

    2012-03-01

    The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

  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. 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.

  3. 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.

  4. 3D geometric split-merge segmentation of brain MRI datasets.

    PubMed

    Marras, Ioannis; Nikolaidis, Nikolaos; Pitas, Ioannis

    2014-05-01

    In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated. A way to find the maximal homogeneity axis of the volume is also introduced. Along this axis, the volume splitting technique divides the entire volume in a number of large homogeneous 3D regions, while at the same time, it defines more clearly small homogeneous regions within the volume in such a way that they have greater probabilities of survival at the subsequent merging step. Region merging criteria are proposed to this end. The presented segmentation method has been applied to brain MRI medical datasets to provide segmentation results when each voxel is composed of one tissue type (hard segmentation). The volume splitting procedure does not require training data, while it demonstrates improved segmentation performance in noisy brain MRI datasets, when compared to the state of the art methods.

  5. An improved brain image classification technique with mining and shape prior segmentation procedure.

    PubMed

    Rajendran, P; Madheswaran, M

    2012-04-01

    The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

  6. 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.

  7. Magnetic nanoparticles: an emerging technology for malignant brain tumor imaging and therapy.

    PubMed

    Wankhede, Mamta; Bouras, Alexandros; Kaluzova, Milota; Hadjipanayis, Costas G

    2012-03-01

    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.

  8. The biology of radiosurgery and its clinical applications for brain tumors

    PubMed Central

    Kondziolka, Douglas; Shin, Samuel M.; Brunswick, Andrew; Kim, Irene; Silverman, Joshua S.

    2015-01-01

    Stereotactic radiosurgery (SRS) was developed decades ago but only began to impact brain tumor care when it was coupled with high-resolution brain imaging techniques such as computed tomography and magnetic resonance imaging. The technique has played a key role in the management of virtually all forms of brain tumor. We reviewed the radiobiological principles of SRS on tissue and how they pertain to different brain tumor disorders. We reviewed the clinical outcomes on the most common indications. This review found that outcomes are well documented for safety and efficacy and show increasing long-term outcomes for benign tumors. Brain metastases SRS is common, and its clinical utility remains in evolution. The role of SRS in brain tumor care is established. Together with surgical resection, conventional radiotherapy, and medical therapies, patients have an expanding list of options for their care. Clinicians should be familiar with radiosurgical principles and expected outcomes that may pertain to different brain tumor scenarios. PMID:25267803

  9. 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

  10. 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

  11. 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.

  12. Radiopotentiation of human brain tumor cells by sodium phenylacetate.

    PubMed

    Ozawa, T; Lu, R M; Hu, L J; Lamborn, K R; Prados, M D; Deen, D F

    1999-08-03

    Phenylacetate (PA) inhibits the growth of tumor cells in vitro and in vivo and shows promise as a relatively nontoxic agent for cancer treatment. A recent report shows that prolonged exposure of cells to low concentrations of PA can enhance the radiation response of brain tumor cells in vitro, opening up the possibility of using this drug to improve the radiation therapy of brain tumor patients. We investigated the cytotoxicity produced by sodium phenylacetate (NaPA) alone and in combination with X-rays in SF-767 human glioblastoma cells and in two medulloblastoma cell lines, Masden and Daoy. Exposure of all three cell lines to relatively low concentrations of NaPA for up to 5 days did not enhance the subsequent cell killing produced by X-irradiation. However, enhanced cell killing was achieved by exposing either oxic or hypoxic cells to relatively high drug concentrations ( > 50-70 mM) for 1 h immediately before X-irradiation. Because central nervous system toxicity can occur in humans at serum concentrations of approximately 6 mM PA, translation of these results into clinical trials will likely require local drug-delivery strategies to achieve drug concentrations that can enhance the radiation response. The safety of such an approach with this drug has not been demonstrated.

  13. Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

    NASA Astrophysics Data System (ADS)

    Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

    2016-10-01

    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.

  14. 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

  15. 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

  16. MR Vascular Fingerprinting in Stroke and Brain Tumors Models

    NASA Astrophysics Data System (ADS)

    Lemasson, B.; Pannetier, N.; Coquery, N.; Boisserand, Ligia S. B.; Collomb, Nora; Schuff, N.; Moseley, M.; Zaharchuk, G.; Barbier, E. L.; Christen, T.

    2016-11-01

    In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases.

  17. MR Vascular Fingerprinting in Stroke and Brain Tumors Models

    PubMed Central

    Lemasson, B.; Pannetier, N.; Coquery, N.; Boisserand, Ligia S. B.; Collomb, Nora; Schuff, N.; Moseley, M.; Zaharchuk, G.; Barbier, E. L.; Christen, T.

    2016-01-01

    In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases. PMID:27883015

  18. MR Vascular Fingerprinting in Stroke and Brain Tumors Models.

    PubMed

    Lemasson, B; Pannetier, N; Coquery, N; Boisserand, Ligia S B; Collomb, Nora; Schuff, N; Moseley, M; Zaharchuk, G; Barbier, E L; Christen, T

    2016-11-24

    In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases.

  19. Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images.

    PubMed

    Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

    2016-10-27

    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.

  20. 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).

  1. 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.

  2. 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.

  3. 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

  4. Disulfiram modulates stemness and metabolism of brain tumor initiating cells in atypical teratoid/rhabdoid tumors

    PubMed Central

    Choi, Seung Ah; Choi, Jung Won; Wang, Kyu-Chang; Phi, Ji Hoon; Lee, Ji Yeoun; Park, Kyung Duk; Eum, Dayoung; Park, Sung-Hye; Kim, Il Han; Kim, Seung-Ki

    2015-01-01

    Background Atypical teratoid/rhabdoid tumors (AT/RT) are among the most malignant pediatric brain tumors. Cells from brain tumors with high aldehyde dehydrogenase (ALDH) activity have a number of characteristics that are similar to brain tumor initiating cells (BTICs). This study aimed to evaluate the therapeutic potential of ALDH inhibition using disulfiram (DSF) against BTICs from AT/RT. Methods Primary cultured BTICs from AT/RT were stained with Aldefluor and isolated by fluorescence activated cell sorting. The therapeutic effect of DSF against BTICs from AT/RT was confirmed in vitro and in vivo. Results AT/RT cells displayed a high expression of ALDH. DSF demonstrated a more potent cytotoxic effect on ALDH+ AT/RT cells compared with standard anticancer agents. Notably, treatment with DSF did not have a considerable effect on normal neural stem cells or fibroblasts. DSF significantly inhibited the ALDH enzyme activity of AT/RT cells. DSF decreased self-renewal ability, cell viability, and proliferation potential and induced apoptosis and cell cycle arrest in ALDH+ AT/RT cells. Importantly, DSF reduced the metabolism of ALDH+ AT/RT cells by increasing the nicotinamide adenine dinucleotide ratio of NAD+/NADH and regulating Silent mating type Information Regulator 2 homolog 1 (SIRT1), nuclear factor-kappaB, Lin28A/B, and miRNA let-7g. Animals in the DSF-treated group demonstrated a reduction of tumor volume (P < .05) and a significant survival benefit (P = .02). Conclusion Our study demonstrated the therapeutic potential of DSF against BTICs from AT/RT and suggested the possibility of ALDH inhibition for clinical application. PMID:25378634

  5. Automated tissue segmentation of MR brain images in the presence of white matter lesions.

    PubMed

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

    2017-01-01

    Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community.

  6. 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

  7. A neuropathology-based approach to epilepsy surgery in brain tumors and proposal for a new terminology use for long-term epilepsy-associated brain tumors.

    PubMed

    Blumcke, Ingmar; Aronica, Eleonora; Urbach, Horst; Alexopoulos, Andreas; Gonzalez-Martinez, Jorge A

    2014-07-01

    Every fourth patient submitted to epilepsy surgery suffers from a brain tumor. Microscopically, these neoplasms present with a wide-ranging spectrum of glial or glio-neuronal tumor subtypes. Gangliogliomas (GG) and dysembryoplastic neuroepithelial tumors (DNTs) are the most frequently recognized entities accounting for 65 % of 1,551 tumors collected at the European Epilepsy Brain Bank (n = 5,842 epilepsy surgery samples). These tumors often present with early seizure onset at a mean age of 16.5 years, with 77 % of neoplasms affecting the temporal lobe. Relapse and malignant progression are rare events in this particular group of brain tumors. Surgical resection should be regarded, therefore, also as important treatment strategy to prevent epilepsy progression as well as seizure- and medication-related comorbidities. The characteristic clinical presentation and broad histopathological spectrum of these highly epileptogenic brain tumors will herein be classified as "long-term epilepsy associated tumors-LEATs". LEATs differ from most other brain tumors by early onset of spontaneous seizures, and conceptually are regarded as developmental tumors to explain their pleomorphic microscopic appearance and frequent association with Focal Cortical Dysplasia Type IIIb. However, the broad neuropathologic spectrum and lack of reliable histopathological signatures make these tumors difficult to classify using the WHO system of brain tumors. As another consequence from poor agreement in published LEAT series, molecular diagnostic data remain ambiguous. Availability of surgical tissue specimens from patients which have been well characterized during their presurgical evaluation should open the possibility to systematically address the origin and epileptogenicity of LEATs, and will be further discussed herein. As a conclusion, the authors propose a novel A-B-C terminology of epileptogenic brain tumors ("epileptomas") which hopefully promote the discussion between neuropathologists

  8. Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory.

    PubMed

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

    2014-10-01

    Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.

  9. Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory

    PubMed Central

    Verma, Nishant; Muralidhar, Gautam S.; Bovik, Alan C.; Cowperthwaite, Matthew C.; Burnett, Mark G.; Markey, Mia K.

    2014-01-01

    Abstract. Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets. PMID:26158060

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. Phenylalanine-coupled solid lipid nanoparticles for brain tumor targeting

    NASA Astrophysics Data System (ADS)

    Kharya, Parul; Jain, Ashish; Gulbake, Arvind; Shilpi, Satish; Jain, Ankit; Hurkat, Pooja; Majumdar, Subrata; Jain, Sanjay K.

    2013-11-01

    The purpose of this study is to investigate the targeting potential of amino acid (phenylalanine)-coupled solid lipid nanoparticles (SLN) loaded with ionically complexed doxorubicin HCl (Dox). Ionic complexation was used to enhance the loading efficiency and release characteristics of water soluble form of Dox. l-Type amino acid transporters (LAT1) are highly expressed on blood brain barrier as well as on many brain cancer cells, thus targeting LAT1 using phenylalanine improved anticancer activity of prepared nanocarrier. The phenylalanine-coupled SLN were characterized by fourier transform infrared spectroscopy, scanning electron microscope, transmission electron microscopy, particle size, zeta potential, entrapment efficiency and in vitro release. The particle size of the resulting SLN was found to be in the range of 163.3 ± 5.2 to 113.0 ± 2.6 nm, with a slightly negative surface charge. In ex vivo study on C6 glioma cell lines, the cellular cytotoxicity of the SLN was highly increased when coupled with phenylalanine. In addition, stealthing sheath of PEG present on the surface of the SLN enhanced the cellular uptake of the SLN on C6 glioma cell line. Results of biodistribution and fluorescence studies clearly revealed that phenylalanine-coupled SLN could deliver high amount of drug into the brain tumor cells and showed the brain-targeting potential.

  17. 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.

  18. 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.

  19. 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.

  20. Segmental duplications and evolutionary plasticity at tumor chromosome break-prone regions

    PubMed Central

    Darai-Ramqvist, Eva; Sandlund, Agneta; Müller, Stefan; Klein, George; Imreh, Stefan; Kost-Alimova, Maria

    2008-01-01

    We have previously found that the borders of evolutionarily conserved chromosomal regions often coincide with tumor-associated deletion breakpoints within human 3p12-p22. Moreover, a detailed analysis of a frequently deleted region at 3p21.3 (CER1) showed associations between tumor breaks and gene duplications. We now report on the analysis of 54 chromosome 3 breaks by multipoint FISH (mpFISH) in 10 carcinoma-derived cell lines. The centromeric region was broken in five lines. In lines with highly complex karyotypes, breaks were clustered near known fragile sites, FRA3B, FRA3C, and FRA3D (three lines), and in two other regions: 3p12.3-p13 (∼75 Mb position) and 3q21.3-q22.1 (∼130 Mb position) (six lines). All locations are shown based on NCBI Build 36.1 human genome sequence. The last two regions participated in three of four chromosome 3 inversions during primate evolution. Regions at 75, 127, and 131 Mb positions carry a large (∼250 kb) segmental duplication (tumor break-prone segmental duplication [TBSD]). TBSD homologous sequences were found at 15 sites on different chromosomes. They were located within bands frequently involved in carcinoma-associated breaks. Thirteen of them have been involved in inversions during primate evolution; 10 were reused by breaks during mammalian evolution; 14 showed copy number polymorphism in man. TBSD sites showed an increase in satellite repeats, retrotransposed sequences, and other segmental duplications. We propose that the instability of these sites stems from specific organization of the chromosomal region, associated with location at a boundary between different CG-content isochores and with the presence of TBSDs and “instability elements,” including satellite repeats and retroviral sequences. PMID:18230801

  1. Objective Ventricle Segmentation in Brain CT with Ischemic Stroke Based on Anatomical Knowledge

    PubMed Central

    Qian, Xiaohua; Lin, Yuan; Zhao, Yue; Yue, Xinyan

    2017-01-01

    Ventricle segmentation is a challenging technique for the development of detection system of ischemic stroke in computed tomography (CT), as ischemic stroke regions are adjacent to the brain ventricle with similar intensity. To address this problem, we developed an objective segmentation system of brain ventricle in CT. The intensity distribution of the ventricle was estimated based on clustering technique, connectivity, and domain knowledge, and the initial ventricle segmentation results were then obtained. To exclude the stroke regions from initial segmentation, a combined segmentation strategy was proposed, which is composed of three different schemes: (1) the largest three-dimensional (3D) connected component was considered as the ventricular region; (2) the big stroke areas were removed by the image difference methods based on searching optimal threshold values; (3) the small stroke regions were excluded by the adaptive template algorithm. The proposed method was evaluated on 50 cases of patients with ischemic stroke. The mean Dice, sensitivity, specificity, and root mean squared error were 0.9447, 0.969, 0.998, and 0.219 mm, respectively. This system can offer a desirable performance. Therefore, the proposed system is expected to bring insights into clinic research and the development of detection system of ischemic stroke in CT. PMID:28271071

  2. 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.

  3. The impact of normalization and segmentation on resting-state brain networks.

    PubMed

    Magalhães, Ricardo; Marques, Paulo; Soares, José; Alves, Victor; Sousa, Nuno

    2015-04-01

    Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. The authors used graph theory analysis applied to resting-state functional magnetic resonance imaging to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented Montreal National Institute (MNI) 152 template, using the Destrieux and subcortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties, and on the network properties levels. The results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors

    DTIC Science & Technology

    2015-10-01

    AWARD NUMBER: W81XWH-14-1-0414 TITLE: Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors PRINCIPAL INVESTIGATOR: Jean Mulcahy...29 Sep 2015 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-14-1-0414 Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors 5b. GRANT...ABSTRACT 200 words most significant findings 15. SUBJECT TERMS autophagy, BRAF, brain tumor. pediatric 16. SECURITY CLASSIFICATION OF: 17

  10. 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

  11. Validation of IR-spectroscopic brain tumor classification

    NASA Astrophysics Data System (ADS)

    Beleites, C.; Steiner, G.; Sobottka, S.; Schackert, G.; Salzer, R.

    2006-02-01

    As a molecular probe of tissue composition, infrared spectroscopic imaging serves as an adjunct to histopathology in detecting and diagnosing disease. In the past it was demonstrated that the IR spectra of brain tumors can be discriminated from one another according to their grade of malignancy. Although classification success rates up to 93% were observed one problem consists in the variation of the models depending on the number of samples used for the development of the classification model. In order to open the path for clinical trials the classification has to be validated. A series of classification models were built using a k-fold cross validation scheme and the classification predictions from the various models were combined to provide an aggregated prediction. The validation highlights instabilities in the models, error rates, sensitivity as well as specificity of the classification and allows the determination of confidence intervals. Better classification models could be achieved by an aggregated prediction. The validation shows that brain tumors can be classified by infrared spectroscopy and the grade of malignancy corresponds reasonably to the histopathological assignment.

  12. "Armed" oncolytic herpes simplex viruses for brain tumor therapy.

    PubMed

    Todo, Tomoki

    2008-01-01

    Genetically engineered, conditionally replicating herpes simplex viruses type 1 (HSV-1) are promising therapeutic agents for brain tumors and other solid cancers. They can replicate in situ, spread and exhibit oncolytic activity via a direct cytocidal effect. One of the advantages of HSV-1 is the capacity to incorporate large and/or multiple transgenes within the viral genome. Oncolytic HSV-1 can therefore be "armed" to add certain functions. Recently, the field of armed oncolytic HSV-1 has drastically advanced, due to development of recombinant HSV-1 generation systems that utilize bacterial artificial chromosome and multiple DNA recombinases. Because antitumor immunity is induced in the course of oncolytic activities of HSV-1, transgenes encoding immunomodulatory molecules have been most frequently used for arming. Other armed oncolytic HSV-1 include those that express antiangiogenic factors, fusogenic membrane glycoproteins, suicide gene products, and proapoptotic proteins. Provided that the transgene product does not interfere with viral replication, such arming of oncolytic HSV-1 results in augmentation of antitumor efficacy. Immediate-early viral promoters are often used to control the arming transgenes, but strict-late viral promoters have been shown useful to restrict the expression in the late stage of viral replication when desirable. Some armed oncolytic HSV-1 have been created for the purpose of noninvasive in vivo imaging of viral infection and replication. Development of a wide variety of armed oncolytic HSV-1 will lead to an establishment of a new genre of therapy for brain tumors as well as other cancers.

  13. Cognitive Remediation Therapy for Brain Tumor Survivors with Cognitive Deficits

    PubMed Central

    Sacks-Zimmerman, Amanda; Liberta, Taylor

    2015-01-01

    Cognitive deficits have been widely observed in patients with primary brain tumors consequent to diagnosis and treatment. Given the early onset and the relatively long survival rate of patients, it seems pertinent to study and refine the techniques used to treat these deficits. The purpose of this article is to discuss cognitive deficits that follow neurosurgical treatment for low-grade gliomas as well as to outline a neuropsychological intervention to treat these deficits, specifically working memory and attention. Cognitive remediation therapy is a neuropsychological intervention that aims to enhance attention, working memory, and executive functioning, thereby diminishing the impact of these deficits on daily functioning. Computerized cognitive remediation training programs facilitate access to treatment through providing online participation. The authors include preliminary results of three participants who have completed the computerized training program as part of an ongoing study that is investigating the efficacy of this program in patients who have undergone treatment for low-grade gliomas. The results so far suggest some improvement in working memory and attention from baseline scores. It is the hope of the present authors to highlight the importance of this treatment in the continuity of care of brain tumor survivors. PMID:26623205

  14. 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 ...

  15. Analysis of plasma free amino acid profiles in canine brain tumors

    PubMed Central

    Utsugi, Shinichi; Azuma, Kazuo; Osaki, Tomohiro; Murahata, Yusuke; Tsuka, Takeshi; Ito, Norihiko; Imagawa, Tomohiro; Okamoto, Yoshiharu

    2017-01-01

    Canine brain tumors are best diagnosed using magnetic resonance imaging (MRI). However, opportunities of MRI examination are restricted due to its limited availability in veterinary facilities; thus, numerous canine brain tumors are diagnosed at an advanced stage. Therefore, development of a noninvasive diagnostic biomarker is required for the early detection of brain tumors. In the present study, plasma free amino acid (PFAA) profiles between dogs with and without brain tumors were compared. A total of 12 dogs with brain tumors, diagnosed based on clinical signs, and on the results of intracranial MRI and/or pathological examination were evaluated. In addition, eight dogs diagnosed with idiopathic epilepsy and 16 healthy dogs were also included. A liquid chromatography system with automated pre-column derivatization functionality was used to measure the levels of 20 amino acids. As a result, the levels of three amino acids (alanine, proline and isoleucine) were increased significantly (1.6-, 1.5- and 1.6-fold, respectively) in the plasma of dogs with brain tumors as compared with the levels in control dogs (all P<0.05). Thus, the PFAA levels of dogs with brain tumors differed from those of healthy dogs. The present study demonstrated that analysis of PFAA levels of dogs with brain tumors may serve as a useful biomarker for the early detection of canine brain tumors. PMID:28357072

  16. Probabilistic multiobject deformable model for MR/SPECT brain image registration and segmentation

    NASA Astrophysics Data System (ADS)

    Nikou, Christophoros; Heitz, Fabrice; Armspach, Jean-Paul

    1999-05-01

    A probabilistic deformable model for the representation of brain structures is described. The statistically learned deformable model represents the relative location of head (skull and scalp) and brain surfaces in MR/SPECT images pairs and accommodates the significant variability of these anatomical structures across different individuals. To provide a training set, a representative collection of 3D MRI volumes of different patients have first been registered to a reference image. The head and brain surfaces of each volume are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MR image in the training set, a vector containing the largest vibration modes describing the head and the brain is created. This random vector is statistically constrained by retaining the most significant variations modes of its Karhunen-Loeve expansion on the training population. By these means, both head and brain surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the probabilistic deformable model are presented: the deformable model-based registration of 3D multimodal (MR/SPECT) brain images and the segmentation of the brain from MRI using the probabilistic constraints embedded in the deformable model. The multi-object deformable model may be considered as a first step towards the development of a general purpose probabilistic anatomical atlas of the brain.

  17. Long-term mobile phone use and brain tumor risk.

    PubMed

    Lönn, Stefan; Ahlbom, Anders; Hall, Per; Feychting, Maria

    2005-03-15

    Handheld mobile phones were introduced in Sweden during the late 1980s. The purpose of this population-based, case-control study was to test the hypothesis that long-term mobile phone use increases the risk of brain tumors. The authors identified all cases aged 20-69 years who were diagnosed with glioma or meningioma during 2000-2002 in certain parts of Sweden. Randomly selected controls were stratified on age, gender, and residential area. Detailed information about mobile phone use was collected from 371 (74%) glioma and 273 (85%) meningioma cases and 674 (71%) controls. For regular mobile phone use, the odds ratio was 0.8 (95% confidence interval: 0.6, 1.0) for glioma and 0.7 (95% confidence interval: 0.5, 0.9) for meningioma. Similar results were found for more than 10 years' duration of mobile phone use. No risk increase was found for ipsilateral phone use for tumors located in the temporal and parietal lobes. Furthermore, the odds ratio did not increase, regardless of tumor histology, type of phone, and amount of use. This study includes a large number of long-term mobile phone users, and the authors conclude that the data do not support the hypothesis that mobile phone use is related to an increased risk of glioma or meningioma.

  18. Segments.

    ERIC Educational Resources Information Center

    Zemsky, Robert; Shaman, Susan; Shapiro, Daniel B.

    2001-01-01

    Presents a market taxonomy for higher education, including what it reveals about the structure of the market, the model's technical attributes, and its capacity to explain pricing behavior. Details the identification of the principle seams separating one market segment from another and how student aspirations help to organize the market, making…

  19. [Factors significant for cerebral circulacion in patients with supratentorial brain tumors].

    PubMed

    Sboev, A Yu; Dolgih, V T; Larkin, V I

    2013-01-01

    Using the Doppler ultrasonography method the condition of brain blood circulation of 90 patients with supratentorial brain tumors (gliomas--43, meningiomas--34, metastasis--9) during pre-surgical period was studied. The factors changing brain blood circulation at patients with with supratentorial brain tumors were brain displacement, increase of intracranial pressure, histologic structure and the first symptoms duration of illness. Localization (for an exception of an occipital lobe) and the size of a tumor directly didn't render influence on blood circulation parameters.

  20. Diffusion Tensor Imaging Based Tissue Segmentation: Validation and Application to the Developing Child and Adolescent Brain

    PubMed Central

    Hasan, Khader M.; Halphen, Christopher; Sankar, Ambika; Eluvathingal, Thomas J.; Kramer, Larry; Stuebing, Karla K.; Ewing-Cobbs, Linda; Fletcher, Jack M.

    2007-01-01

    We present and validate a novel diffusion tensor imaging (DTI) approach for segmenting the human whole-brain into partitions representing grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The approach utilizes the contrast among tissue types in the DTI anisotropy vs. diffusivity rotational invariant space. The DTI-based whole-brain GM and WM fractions (GMf and WMf) are contrasted with the fractions obtained from conventional magnetic resonance imaging (cMRI) tissue segmentation (or clustering) methods that utilized dual echo (proton density-weighted (PDw), and spin-spin relaxation-weighted (T2w) contrast, in addition to spin-lattice relaxation weighted (T1w) contrasts acquired in the same imaging session and covering the same volume. In addition to good correspondence with cMRI estimates of brain volume, the DTI-based accurately depicts expected age vs. WM and GM volume-to-total intracranial brain volume percentage trends on the rapidly developing brains of a cohort of 29 children (6–18 years). This approach promises to extend DTI utility to both micro and macrostructural aspects of tissue organization. PMID:17166746

  1. A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images

    PubMed Central

    Shen, Xuanjing; Feng, Yuncong

    2017-01-01

    Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level. Then, two adjacent regions are merged in each iteration by a new designed scheme, and a threshold is removed each time. To improve the accuracy of the merger operation, variance and probability are used as energy. No matter how many the thresholds are, the time complexity of the algorithm is stable at O(L). Finally, the experiment is conducted on many MR brain images to verify the performance of the proposed algorithm. Experiment results show that our method can reduce the running time effectively and obtain segmentation results with high accuracy.

  2. Automated segmentation of murine lung tumors in x-ray micro-CT images

    NASA Astrophysics Data System (ADS)

    Swee, Joshua K. Y.; Sheridan, Clare; de Bruin, Elza; Downward, Julian; Lassailly, Francois; Pizarro, Luis

    2014-03-01

    Recent years have seen micro-CT emerge as a means of providing imaging analysis in pre-clinical study, with in-vivo micro-CT having been shown to be particularly applicable to the examination of murine lung tumors. Despite this, existing studies have involved substantial human intervention during the image analysis process, with the use of fully-automated aids found to be almost non-existent. We present a new approach to automate the segmentation of murine lung tumors designed specifically for in-vivo micro-CT-based pre-clinical lung cancer studies that addresses the specific requirements of such study, as well as the limitations human-centric segmentation approaches experience when applied to such micro-CT data. Our approach consists of three distinct stages, and begins by utilizing edge enhancing and vessel enhancing non-linear anisotropic diffusion filters to extract anatomy masks (lung/vessel structure) in a pre-processing stage. Initial candidate detection is then performed through ROI reduction utilizing obtained masks and a two-step automated segmentation approach that aims to extract all disconnected objects within the ROI, and consists of Otsu thresholding, mathematical morphology and marker-driven watershed. False positive reduction is finally performed on initial candidates through random-forest-driven classification using the shape, intensity, and spatial features of candidates. We provide validation of our approach using data from an associated lung cancer study, showing favorable results both in terms of detection (sensitivity=86%, specificity=89%) and structural recovery (Dice Similarity=0.88) when compared against manual specialist annotation.

  3. Brain tumor initiating cells adapt to restricted nutrition through preferential glucose uptake.

    PubMed

    Flavahan, William A; Wu, Qiulian; Hitomi, Masahiro; Rahim, Nasiha; Kim, Youngmi; Sloan, Andrew E; Weil, Robert J; Nakano, Ichiro; Sarkaria, Jann N; Stringer, Brett W; Day, Bryan W; Li, Meizhang; Lathia, Justin D; Rich, Jeremy N; Hjelmeland, Anita B

    2013-10-01

    Like all cancers, brain tumors require a continuous source of energy and molecular resources for new cell production. In normal brain, glucose is an essential neuronal fuel, but the blood-brain barrier limits its delivery. We now report that nutrient restriction contributes to tumor progression by enriching for brain tumor initiating cells (BTICs) owing to preferential BTIC survival and to adaptation of non-BTICs through acquisition of BTIC features. BTICs outcompete for glucose uptake by co-opting the high affinity neuronal glucose transporter, type 3 (Glut3, SLC2A3). BTICs preferentially express Glut3, and targeting Glut3 inhibits BTIC growth and tumorigenic potential. Glut3, but not Glut1, correlates with poor survival in brain tumors and other cancers; thus, tumor initiating cells may extract nutrients with high affinity. As altered metabolism represents a cancer hallmark, metabolic reprogramming may maintain the tumor hierarchy and portend poor prognosis.

  4. An evaluation of four automatic methods of segmenting the subcortical structures in the brain.

    PubMed

    Babalola, Kolawole Oluwole; Patenaude, Brian; Aljabar, Paul; Schnabel, Julia; Kennedy, David; Crum, William; Smith, Stephen; Cootes, Tim; Jenkinson, Mark; Rueckert, Daniel

    2009-10-01

    The automation of segmentation of subcortical structures in the brain is an active research area. We have comprehensively evaluated four novel methods of fully automated segmentation of subcortical structures using volumetric, spatial overlap and distance-based measures. Two methods are atlas-based - classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a brain atlas (EMS), and two incorporate statistical models of shape and appearance - profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed better than the others according to all three classes of metrics. In summary over all structures, the ranking by the Dice coefficient was CFL, BAM, joint EMS and PAM. The Hausdorff distance ranked the methods as CFL, joint PAM and BAM, EMS, whilst percentage absolute volumetric difference ranked them as joint CFL and PAM, joint BAM and EMS. Furthermore, as we had four methods of performing segmentation, we investigated whether the results obtained by each method were more similar to each other than to the manual segmentations using Williams' Index. Reassuringly, the Williams' Index was close to 1 for most subjects (mean=1.02, sd=0.05), indicating better agreement of each method with the gold standard than with the other methods. However, 2% of cases (mainly amygdala and nucleus accumbens) had values outside 3 standard deviations of the mean.

  5. Using Ferumoxytol-Enhanced MRI to Measure Inflammation in Patients With Brain Tumors or Other Conditions of the CNS

    ClinicalTrials.gov

    2017-02-21

    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

  6. 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.

  7. Multi-structure segmentation of multi-modal brain images using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Kim, Eun Young; Johnson, Hans

    2010-03-01

    A method for simultaneous segmentation of multiple anatomical brain structures from multi-modal MR images has been developed. An artificial neural network (ANN) was trained from a set of feature vectors created by a combination of high-resolution registration methods, atlas based spatial probability distributions, and a training set of 16 expert traced data sets. A set of feature vectors were adapted to increase performance of ANN segmentation; 1) a modified spatial location for structural symmetry of human brain, 2) neighbors along the priors descent for directional consistency, and 3) candidate vectors based on the priors for the segmentation of multiple structures. The trained neural network was then applied to 8 data sets, and the results were compared with expertly traced structures for validation purposes. Comparing several reliability metrics, including a relative overlap, similarity index, and intraclass correlation of the ANN generated segmentations to a manual trace are similar or higher to those measures previously developed methods. The ANN provides a level of consistency between subjects and time efficiency comparing human labor that allows it to be used for very large studies.

  8. Level set method coupled with Energy Image features for brain MR image segmentation.

    PubMed

    Punga, Mirela Visan; Gaurav, Rahul; Moraru, Luminita

    2014-06-01

    Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

  9. Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion

    PubMed Central

    Mulder, Inge A.; Khmelinskii, Artem; Dzyubachyk, Oleh; de Jong, Sebastiaan; Rieff, Nathalie; Wermer, Marieke J. H.; Hoehn, Mathias; Lelieveldt, Boudewijn P. F.; van den Maagdenberg, Arn M. J. M.

    2017-01-01

    Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets. PMID:28197090

  10. Diffeomorphic registration with self-adaptive spatial regularization for the segmentation of non-human primate brains.

    PubMed

    Risser, Laurent; Dolius, Lionel; Fonta, Caroline; Mescam, Muriel

    2014-01-01

    Cerebral aging has been linked to structural and functional changes in the brain throughout life. Here, we study the marmoset, a small non-human primate, in order to get insights into the mechanisms of brain aging in normal and pathological conditions. Imaging the brain of small animals with techniques such as MRI, quickly becomes a challenging task when compared with human brain imaging. Very often, a simple pre-processing step such as brain extraction cannot be achieved with classical tools. In this paper, we propose a diffeomorphic registration algorithm, which makes use of learned constraints to propagate the manual segmentation of a marmoset brain template to other MR images of marmoset brains. The main methological contribution of our paper is to explore a new strategy to automatically tune the spatial regularization of the deformations. Results show that we obtain a robust segmentation of the brain, even for images with a low contrast.

  11. 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.

  12. 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

  13. Tumor cell survival dependence on helical tomotherapy, continuous arc and segmented dose delivery

    NASA Astrophysics Data System (ADS)

    Yang, Wensha; Wang, Li; Larner, James; Read, Paul; Benedict, Stan; Sheng, Ke

    2009-11-01

    The temporal pattern of radiation delivery has been shown to influence the tumor cell survival fractions for the same radiation dose. To study the effect more specifically for state of the art rotational radiation delivery modalities, 2 Gy of radiation dose was delivered to H460 lung carcinoma, PC3 prostate cancer cells and MCF-7 breast tumor cells by helical tomotherapy (HT), seven-field LINAC (7F), and continuous dose delivery (CDD) over 2 min that simulates volumetric rotational arc therapy. Cell survival was measured by the clonogenic assay. The number of viable H460 cell colonies was 23.2 ± 14.4% and 27.7 ± 15.6% lower when irradiated by CDD compared with HT and 7F, respectively, and the corresponding values were 36.8 ± 18.9% and 35.3 ± 18.9% lower for MCF7 cells (p < 0.01). The survival of PC3 was also lower when irradiated by CDD than by HT or 7F but the difference was not as significant (p = 0.06 and 0.04, respectively). The higher survival fraction from HT delivery was unexpected because 90% of the 2 Gy was delivered in less than 1 min at a significantly higher dose rate than the other two delivery techniques. The results suggest that continuous dose delivery at a constant dose rate results in superior in vitro tumor cell killing compared with prolonged, segmented or variable dose rate delivery.

  14. Isolated angiitis in the hypothalamus mimicking brain tumor.

    PubMed

    Tsutsumi, Satoshi; Ito, Masanori; Yasumoto, Yukimasa; Kaneda, Kazuhiko

    2008-01-01

    A 64-year-old female presented with exaggerating somnolence without contributory medical and lifestyle histories. She was not aware of any preceding infection or headache. Cerebral magnetic resonance imaging demonstrated an isolated enhanced mass in the hypothalamus without meningeal enhancement. Blood and cerebrospinal fluid examinations showed no significant findings except for hypernatremia and hyperprolactinemia. She underwent an open biopsy via the interhemispheric route. Histological examination revealed marked perivascular lymphocytic aggregation with polyclonal immunostaining both for B and T lymphocytes. No findings suggestive of underlying malignancy were recognized. Extensive work-up aiming at systemic vasculitis and lymphoma revealed no signs of extracranial lesion, so the most probable diagnosis was isolated angiitis in the hypothalamus. Angiitis may originate from the hypothalamus and should be considered in the differential diagnosis of hypothalamic lesion mimicking brain tumor on neuroimaging.

  15. Brain Tumor Epidemiology - A Hub within Multidisciplinary Neuro-oncology. Report on the 15th Brain Tumor Epidemiology Consortium (BTEC) Annual Meeting, Vienna, 2014.

    PubMed

    Woehrer, Adelheid; Lau, Ching C; Prayer, Daniela; Bauchet, Luc; Rosenfeld, Myrna; Capper, David; Fisher, Paul G; Kool, Marcel; Müller, Martin; Kros, Johan M; Kruchko, Carol; Wiemels, Joseph; Wrensch, Margaret; Danysh, Heather E; Zouaoui, Sonia; Heck, Julia E; Johnson, Kimberly J; Qi, Xiaoyang; O'Neill, Brian P; Afzal, Samina; Scheurer, Michael E; Bainbridge, Matthew N; Nousome, Darryl; Bahassi, El Mustapha; Hainfellner, Johannes A; Barnholtz-Sloan, Jill S

    2015-01-01

    The Brain Tumor Epidemiology Consortium (BTEC) is an open scientific forum, which fosters the development of multi-center, international and inter-disciplinary collaborations. BTEC aims to develop a better understanding of the etiology, outcomes, and prevention of brain tumors (http://epi.grants.cancer.gov/btec/). The 15th annual Brain Tumor Epidemiology Consortium Meeting, hosted by the Austrian Societies of Neuropathology and Neuro-oncology, was held on September 9 - 11, 2014 in Vienna, Austria. The meeting focused on the central role of brain tumor epidemiology within multidisciplinary neuro-oncology. Knowledge of disease incidence, outcomes, as well as risk factors is fundamental to all fields involved in research and treatment of patients with brain tumors; thus, epidemiology constitutes an important link between disciplines, indeed the very hub. This was reflected by the scientific program, which included various sessions linking brain tumor epidemiology with clinical neuro-oncology, tissue-based research, and cancer registration. Renowned experts from Europe and the United States contributed their personal perspectives stimulating further group discussions. Several concrete action plans evolved for the group to move forward until next year's meeting, which will be held at the Mayo Clinic at Rochester, MN, USA.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. The exciting potential of nanotherapy in brain-tumor targeted drug delivery approaches

    PubMed Central

    Agrahari, Vivek

    2017-01-01

    Delivering therapeutics to the central nervous system (CNS) and brain-tumor has been a major challenge. The current standard treatment approaches for the brain-tumor comprise of surgical resection followed by immunotherapy, radiotherapy, and chemotherapy. However, the current treatments are limited in providing significant benefits to the patients and despite recent technological advancements; brain-tumor is still challenging to treat. Brain-tumor therapy is limited by the lack of effective and targeted strategies to deliver chemotherapeutic agents across the blood-brain barrier (BBB). The BBB is the main obstacle that must be overcome to allow compounds to reach their targets in the brain. Recent advances have boosted the nanotherapeutic approaches in providing an attractive strategy in improving the drug delivery across the BBB and into the CNS. Compared to conventional formulations, nanoformulations offer significant advantages in CNS drug delivery approaches. Considering the above facts, in this review, the physiological/anatomical features of the brain-tumor and the BBB are briefly discussed. The drug transport mechanisms at the BBB are outlined. The approaches to deliver chemotherapeutic drugs across the CNS into the brain-tumor using nanocarriers are summarized. In addition, the challenges that need to be addressed in nanotherapeutic approaches for their enhanced clinical application in brain-tumor therapy are discussed.

  2. 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

  3. DNMTs as potential therapeutic targets in high-risk pediatric embryonal brain tumors.

    PubMed

    Sin-Chan, Patrick; Huang, Annie

    2014-10-01

    Malignant brain tumors, which are the leading cause of cancer-related morbidity and mortality in children, span a wide spectrum of diseases with distinct clinical phenotypes but may share remarkably similar morphologic features. Until recently, few molecular markers of childhood brain tumors have been identified, which has limited therapeutic advances. Recent global genomic studies have enabled robust molecular classification of childhood brain tumors and the identification and consolidation of rare, seemingly disparate clinical entities. It is now increasingly evident that deregulation of epigenetic processes contributes substantially to heterogeneity in tumor phenotypes and comprise significant drivers of cancer initiation and progression. Specifically, DNA hypermethylation and silencing of critical tumor suppressor genes by DNA methyltransferases (DNMT) has emerged as an important and fundamental mechanism in brain tumor pathogenesis. These observations have been underscored by the recent discovery of TTYH1-C19MC gene fusions in an aggressive pediatric embryonal brain tumor, which results in deregulation and increased expression of a neural-specific DNMT3B isoform in C19MC-associated brain tumors. Our observations that pharmacological inhibitors of DNMTs and histone deacetylases significantly inhibit growth of cells derived from C19MC-associated tumors indicate targeting of epigenomic modifiers as a novel therapeutic approach for these highly treatment-resistant tumors.

  4. The incidence of second brain tumors related to cranial irradiation.

    PubMed

    Marta, Gustavo Nader; Murphy, Erin; Chao, Samuel; Yu, Jennifer S; Suh, John H

    2015-03-01

    Secondary brain tumor (SBT) is a devastating complication of cranial irradiation (CI). We reviewed the literature to determine the incidence of SBT as related to specific radiation therapy (RT) treatment modalities. The relative risk of radiation-associated SBT after conventional and conformal RT is well established and ranges from 5.65 to 10.9; latent time to develop second tumor ranges from 5.8 to 22.4 years, depending on radiation dose and primary disease. Theories and dosimetric models suggest that intensity-modulated radiation therapy may result in an increased risk of SBT, but clinical evidence is limited. The incidence of stereotactic radiosurgery-related SBT is low. Initial data suggest that no increased risk from proton therapy and dosimetric models predict a lower incidence of SBT compared with photons. In conclusion, the incidence of SBT related to CI is low. Longer follow-up is needed to clarify the impact of intensity-modulated radiation therapy, proton therapy and other developing technologies.

  5. Aptamer for imaging and therapeutic targeting of brain tumor glioblastoma.

    PubMed

    Delač, Mateja; Motaln, Helena; Ulrich, Henning; Lah, Tamara T

    2015-09-01

    Aptamers are short single-stranded nucleic acids (RNA or ssDNA), identified by an in vitro selection process, denominated SELEX, from a partially random oligonucleotide library. They bind to a molecular target, a protein or other complex macromolecular structures of interest with high affinity and specificity, comparable to those of antibodies. Recently, aptamer selection protocols were developed for targeting living cells, including tumors. Chemical modifications of the aptamers and modalities of their detection and delivery systems are already available with high selectivity and targeting ability for the desired cancer cell type, making them promising for diagnosis and therapy. Glioblastoma multiformae represents the most malignant and fatal stage of glioma, and is also the most frequent brain tumor. Glioblastoma-specific aptamers were developed by either targeting the whole cell surface or known glioma biomarkers. These aptamers may gain importance for imaging, tumor cell isolation from biopsies and drug delivery. In biomedical imaging techniques, aptamers coupled with radionuclide or fluorescent labels, bioconjugates and nanoparticles offer an advanced, noninvasive manner for defining the glioblastoma tissue border. Though single modality aptamer imaging probes have some limitations, these are overcome by the use of multimodal probes. Due to selectivity and chemical characteristics, aptamers can be coupled to functionalized nanoparticles and loaded with a drug, appeared promising for in vivo targeting of glioblastoma. Finally, aptamers are effective mediators for gene silencing when coupled to small interfering RNA and a viral vector, thus providing a novel tool with enhanced targeting capability in drug delivery, designed for tailored treatment of glioblastoma patients.

  6. A Mathematical Model to Elucidate Brain Tumor Abrogation by Immunotherapy with T11 Target Structure

    PubMed Central

    Chaudhuri, Swapna

    2015-01-01

    T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep erythrocytes, reverses the immune suppressed state of brain tumor induced animals by boosting the functional status of the immune cells. This study aims at aiding in the design of more efficacious brain tumor therapies with T11 target structure. We propose a mathematical model for brain tumor (glioma) and the immune system interactions, which aims in designing efficacious brain tumor therapy. The model encompasses considerations of the interactive dynamics of glioma cells, macrophages, cytotoxic T-lymphocytes (CD8+ T-cells), TGF-β, IFN-γ and the T11TS. The system undergoes sensitivity analysis, that determines which state variables are sensitive to the given parameters and the parameters are estimated from the published data. Computer simulations were used for model verification and validation, which highlight the importance of T11 target structure in brain tumor therapy. PMID:25955428

  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. 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

  9. Fotemustine in the treatment of brain primary tumors and metastases.

    PubMed

    Khayat, D; Giroux, B; Berille, J; Cour, V; Gerard, B; Sarkany, M; Bertrand, P; Bizzari, J P

    1994-01-01

    Fotemustine is a new chloroethylnitrosourea characterized by the grafting of a phosphonoalanine group onto a nitrosourea radical. Clinical studies using fotemustine have been conducted in malignant glioma, brain metastasis of non-small cell lung cancer, and disseminated malignant melanoma. In recurrent malignant glioma, fotemustine has been used as a single agent: assessed by computed tomography scan, after 8 weeks, the objective response rate was 26.3% among 38 evaluable patients. Median duration of response was 33 weeks. The main toxicity was hematological (thrombocytopenia and leucopenia). A trial with high-dose fotemustine and autologous bone marrow rescue in newly diagnosed glioma was conducted in 26 patients, and 6 showed a partial response. The median overall survival was approximately 11 months. Myelosuppression was noted in all patients except 1, and other toxicity reported was central nervous system toxicity and epigastric pain. Combined with radiotherapy in 55 patients, a 29% response rate was observed, and this combination was well tolerated and easily manageable on an outpatient basis. Finally, fotemustine has been used intraarterially, with 10 objective responses observed among 26 evaluable patients. In brain metastases of non-small cell lung cancer, fotemustine proved to be active with a response rate of 16.7%. Combined with cisplatinum, fotemustine is still under study, but preliminary results are promising. In cerebral metastases of disseminated malignant melanoma, fotemustine has been evaluated in a total of 140 patients in the various studies: median response rate is 24.3%, ranging from 8.3% to 60.0%. Fotemustine appears to be a good candidate in the treatment of primary brain tumors and metastases.

  10. Laser interstitial thermal therapy in treatment of brain tumors--the NeuroBlate System.

    PubMed

    Mohammadi, Alireza M; Schroeder, Jason L

    2014-03-01

    Treatment of brain tumors remains challenging. Cytoreductive surgery is used as the first line treatment for most brain tumors. However complete, curative, resection is not achievable in many tumors leading to the need for adjuvant chemotherapy and radiation therapy. Laser interstitial thermal therapy (LITT) is a minimally invasive cytoreductive treatment. A low voltage laser is used to induce hyperthermia and to kill tumor cells. The extent of thermal damage is controlled through use of real-time MR-thermography guidance. Initial results have shown the feasibility of LITT for a variety of brain pathologies. LITT can be considered as an alternative type of surgery for difficult to access brain tumors and also for tumors in patients who are deemed high risk for more traditional surgery. Randomized trials are currently planned to continue assessing the efficacy of LITT and long-term follow-up data are awaited.

  11. Semi-automated 3D segmentation of major tracts in the rat brain: comparing DTI with standard histological methods.

    PubMed

    Gyengesi, Erika; Calabrese, Evan; Sherrier, Matthew C; Johnson, G Allan; Paxinos, George; Watson, Charles

    2014-03-01

    Researchers working with rodent models of neurological disease often require an accurate map of the anatomical organization of the white matter of the rodent brain. With the increasing popularity of small animal MRI techniques, including diffusion tensor imaging (DTI), there is considerable interest in rapid segmentation methods of neurological structures for quantitative comparisons. DTI-derived tractography allows simple and rapid segmentation of major white matter tracts, but the anatomic accuracy of these computer-generated fibers is open to question and has not been rigorously evaluated in the rat brain. In this study, we examine the anatomic accuracy of tractography-based segmentation in the adult rat brain. We analysed 12 major white matter pathways using semi-automated tractography-based segmentation alongside manual segmentation of Gallyas silver-stained histology sections. We applied four fiber-tracking algorithms to the DTI data-two integration methods and two deflection methods. In many cases, tractography-based segmentation closely matched histology-based segmentation; however different tractography algorithms produced dramatically different results. Results suggest that certain white matter pathways are more amenable to tractography-based segmentation than others. We believe that these data will help researchers decide whether it is appropriate to use tractography-based segmentation of white matter structures for quantitative DTI-based analysis of neurologic disease models.

  12. Apoptosis imaging for monitoring DR5 antibody accumulation and pharmacodynamics in brain tumors non-invasively

    PubMed Central

    Weber, Thomas G.; Osl, Franz; Renner, Anja; Pöschinger, Thomas; Galbán, Stefanie; Rehemtulla, Alnawaz; Scheuer, Werner

    2014-01-01

    High grade gliomas often possess an impaired blood-brain barrier (BBB) which allows delivery of large molecules to brain tumors. However, achieving optimal drug concentrations in brain tumors remains a significant hurdle for treating patients successfully. Thus, detailed investigations of drug activities in gliomas are needed. To investigate BBB penetration, pharmacodynamics and tumor retention kinetics, we studied an agonistic DR5 antibody in a brain tumor xenograft model to investigate a non-invasive imaging method for longitudinal monitoring of apoptosis induction by this antibody. Brain tumors were induced by intracranial (i.c.) implantation of a luciferase-expressing tumor cell line as a reporter. To quantify accumulation of anti-DR5 in brain tumors, we generated a dose response curve for apoptosis induction after i.c. delivery of fluorescence-labeled anti-DR5 at different dosages. Assuming 100% drug delivery after i.c. application, the amount of accumulated antibody after i.v. application was calculated relative to its apoptosis induction. We found that up to 0.20–0.97% of antibody delivered i.v. reached the brain tumor, but that apoptosis induction declined quickly within 24 hours. These results were confirmed by 3D fluorescence microscopy of antibody accumulation in explanted brains. Nonetheless, significant antitumor efficacy was documented after anti-DR5 delivery. We further demonstrated that antibody crossing the BBB was facilitated its impairment in brain tumors. These imaging methods enable the quantification of antibody accumulation and pharmacodynamics in brain tumors, offering a holistic approach for assessment of CNS targeting drugs. PMID:24509903

  13. Sequential activation of a segmented ground pad reduces skin heating during radiofrequency tumor ablation: optimization via computational models.

    PubMed

    Schutt, David J; Haemmerich, Dieter

    2008-07-01

    Radiofrequency (RF) ablation has become an accepted treatment modality for unresectable tumors. The need for larger ablation zones has resulted in increased RF generator power. Skin burns due to ground pad heating are increasingly limiting further increases in generator power, and thus, ablation zone size. We investigated a method for reducing ground pad heating in which a commercial ground pad is segmented into multiple ground electrodes, with sequential activation of ground electrode subsets. We created finite-element method computer models of a commercial ground pad (14 x 23 cm) and compared normal operation of a standard pad to sequential activation of a segmented pad (two to five separate ground electrode segments). A constant current of 1 A was applied for 12 min in all simulations. Time periods during sequential activation simulations were adjusted to keep the leading edge temperatures at each ground electrode equal. The maximum temperature using standard activation of the commercial pad was 41.7 degrees C. For sequential activation of a segmented pad, the maximum temperature ranged from 39.3 degrees C (five segments) to 40.9 degrees C (two segments). Sequential activation of a segmented ground pad resulted in lower tissue temperatures. This method may reduce the incidence of ground pad burns and enable the use of higher power generators during RF tumor ablation.

  14. Sequential Activation of a Segmented Ground Pad Reduces Skin Heating During Radiofrequency Tumor Ablation: Optimization via Computational Models

    PubMed Central

    Schutt, David J.; Haemmerich, Dieter

    2009-01-01

    Radiofrequency (RF) ablation has become an accepted treatment modality for unresectable tumors. The need for larger ablation zones has resulted in increased RF generator power. Skin burns due to ground pad heating are increasingly limiting further increases in generator power, and thus, ablation zone size. We investigated a method for reducing ground pad heating in which a commercial ground pad is segmented into multiple ground electrodes, with sequential activation of ground electrode subsets. We created finite-element method computer models of a commercial ground pad (14 × 23 cm) and compared normal operation of a standard pad to sequential activation of a segmented pad (two to five separate ground electrode segments). A constant current of 1 A was applied for 12 min in all simulations. Time periods during sequential activation simulations were adjusted to keep the leading edge temperatures at each ground electrode equal. The maximum temperature using standard activation of the commercial pad was 41.7 °C. For sequential activation of a segmented pad, the maximum temperature ranged from 39.3 °C (five segments) to 40.9 °C (two segments). Sequential activation of a segmented ground pad result