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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. Copyright © 2016 Elsevier B.V. All rights reserved.

  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. State of the art survey on MRI brain tumor segmentation.

    PubMed

    Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

    2013-10-01

    Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI

    NASA Astrophysics Data System (ADS)

    Pei, Linmin; Reza, Syed M. S.; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M.

    2017-03-01

    In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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

    PubMed

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

    2015-10-01

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

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

    NASA Astrophysics Data System (ADS)

    Gupta, Anjali; Pahuja, Gunjan

    2017-08-01

    The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

  16. 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.

  17. 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.

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

    PubMed

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

    2006-12-01

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

  19. 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

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

    NASA Astrophysics Data System (ADS)

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-03-01

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

  1. An automatic method of brain tumor segmentation from MRI volume based on the symmetry of brain and level set method

    NASA Astrophysics Data System (ADS)

    Li, Xiaobing; Qiu, Tianshuang; Lebonvallet, Stephane; Ruan, Su

    2010-02-01

    This paper presents a brain tumor segmentation method which automatically segments tumors from human brain MRI image volume. The presented model is based on the symmetry of human brain and level set method. Firstly, the midsagittal plane of an MRI volume is searched, the slices with potential tumor of the volume are checked out according to their symmetries, and an initial boundary of the tumor in the slice, in which the tumor is in the largest size, is determined meanwhile by watershed and morphological algorithms; Secondly, the level set method is applied to the initial boundary to drive the curve evolving and stopping to the appropriate tumor boundary; Lastly, the tumor boundary is projected one by one to its adjacent slices as initial boundaries through the volume for the whole tumor. The experiment results are compared with hand tracking of the expert and show relatively good accordance between both.

  2. 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.

  3. 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.

  4. Fast and robust brain tumor segmentation using level set method with multiple image information.

    PubMed

    Lok, Ka Hei; Shi, Lin; Zhu, Xianlun; Wang, Defeng

    2017-01-01

    Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. Assessing Variability in Brain Tumor Segmentation to Improve Volumetric Accuracy and Characterization of Change.

    PubMed

    Rios Piedra, Edgar A; Taira, Ricky K; El-Saden, Suzie; Ellingson, Benjamin M; Bui, Alex A T; Hsu, William

    2016-02-01

    Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), providing a more precise description of disease progression to better inform clinical decision-making and treatment planning. While a multitude of segmentation approaches exist, inherent variability in the results of these algorithms may incorrectly indicate changes in tumor volume. In this work, we present a systematic approach to characterize variability in tumor boundaries that utilizes equivalence tests as a means to determine whether a tumor volume has significantly changed over time. To demonstrate these concepts, 32 MRI studies from 8 patients were segmented using four different approaches (statistical classifier, region-based, edge-based, knowledge-based) to generate different regions of interest representing tumor extent. We showed that across all studies, the average Dice coefficient for the superset of the different methods was 0.754 (95% confidence interval 0.701-0.808) when compared to a reference standard. We illustrate how variability obtained by different segmentations can be used to identify significant changes in tumor volume between sequential time points. Our study demonstrates that variability is an inherent part of interpreting tumor segmentation results and should be considered as part of the interpretation process.

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

    PubMed

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

    2005-12-01

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

  11. Brain tumor segmentation using holistically nested neural networks in MRI images.

    PubMed

    Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

    2017-07-24

    Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of

  12. SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

    SciTech Connect

    Liu, Y; Stojadinovic, S; Jiang, S; Timmerman, R; Abdulrahman, R; Nedzi, L; Gu, X

    2016-06-15

    Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0

  13. 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.

  14. Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model

    PubMed Central

    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. PMID:21995070

  15. 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

  16. Brain tumor segmentation in MRI by using the fuzzy connectedness method

    NASA Astrophysics Data System (ADS)

    Liu, Jian-Guo; Udupa, Jayaram K.; Hackney, David; Moonis, Gul

    2001-07-01

    The aim of this paper is the precise and accurate quantification of brain tumor via MRI. This is very useful in evaluating disease progression, response to therapy, and the need for changes in treatment plans. We use multiple MRI protocols including FLAIR, T1, and T1 with Gd enhancement to gather information about different aspects of the tumor and its vicinity- edema, active regions, and scar left over due to surgical intervention. We have adapted the fuzzy connectedness framework to segment tumor and to measure its volume. The method requires only limited user interaction in routine clinical MRI. The first step in the process is to apply an intensity normalization method to the images so that the same body region has the same tissue meaning independent of the scanner and patient. Subsequently, a fuzzy connectedness algorithm is utilized to segment the different aspects of the tumor. The system has been tested, for its precision, accuracy, and efficiency, utilizing 40 patient studies. The percent coefficient of variation (% CV) in volume due to operator subjectivity in specifying seeds for fuzzy connectedness segmentation is less than 1%. The mean operator and computer time taken per study is 3 minutes. The package is designed to run under operator supervision. Delineation has been found to agree with the operators' visual inspection most of the time except in some cases when the tumor is close to the boundary of the brain. In the latter case, the scalp is included in the delineation and an operator has to exclude this manually. The methodology is rapid, robust, consistent, yielding highly reproducible measurements, and is likely to become part of the routine evaluation of brain tumor patients in our health system.

  17. 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.

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

    PubMed Central

    Vijayakumar, C.; Gharpure, Damayanti Chandrashekhar

    2011-01-01

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

  19. Deep learning for segmentation of brain tumors: can we train with images from different institutions?

    NASA Astrophysics Data System (ADS)

    Paredes, David; Saha, Ashirbani; Mazurowski, Maciej A.

    2017-03-01

    Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. However, in the environment which fosters data-sharing and collection of multi-institutional datasets, a question arises: does training with data from another institution with potentially different imaging equipment, contrast protocol, and patient population impact the segmentation performance of the CNN? Our study presents preliminary data towards answering this question. Specifically, we used MRI data of glioblastoma (GBM) patients for two institutions present in The Cancer Imaging Archive. We performed a process of training and testing CNN multiple times such that half of the time the CNN was tested on data from the same institution that was used for training and half of the time it was tested on another institution, keeping the training and testing set size constant. We observed a decrease in performance as measured by Dice coefficient when the CNN was trained with data from a different institution as compared to training with data from the same institution. The changes in performance for the entire tumor and for four different labels within the tumor were: 0.72 to 0.65 (p=0.06), 0.61 to 0.58 (p=0.49), 0.54 to 0.51 (p=0.82), 0.31 to 0.24 (p<0.03), and 0.43 to 0.31(p<0.003) respectively. In summary, we found that while data across institutions can be used for development of CNNs, this might be associated with a decrease in performance.

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

    NASA Astrophysics Data System (ADS)

    Mazzara, Gloria Patrika

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

  1. 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

  2. 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.

  3. 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.

  4. 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, ...

  5. Understanding Brain Tumors

    MedlinePlus

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

  6. Brain Tumor Diagnosis

    MedlinePlus

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

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

    SciTech Connect

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

    2014-05-15

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

  8. 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

  9. 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.

  10. Metastatic brain tumor

    MedlinePlus

    Brain tumor - metastatic (secondary); Cancer - brain tumor (metastatic) ... For many people with metastatic brain tumors, the cancer is not curable. It will eventually spread to other areas of the body. Prognosis depends on the type of tumor and ...

  11. 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 ...

  12. 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 ...

  13. 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 ...

  14. 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.

  15. Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

    PubMed

    Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

    2014-03-01

    Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

  16. 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 ...

  17. 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: ...

  18. 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.

  19. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

    PubMed

    Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

    2016-01-01

    To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

  20. 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

  1. Automated tumor volumetry using computer-aided image segmentation.

    PubMed

    Gaonkar, Bilwaj; Macyszyn, Luke; 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

    2015-05-01

    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. 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. 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. 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. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

  2. Brain Tumor Surgery

    MedlinePlus

    ... Proton Therapy Alternative & Integrative Medicine Clinical Trials GBM AGILE TTFields – Optune™ Brain Tumor Treatment Locations Treatment Side ... Proton Therapy Alternative & Integrative Medicine Clinical Trials GBM AGILE TTFields – Optune™ Brain Tumor Treatment Locations Treatment Side ...

  3. 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, ...

  4. Epilepsy and brain tumors.

    PubMed

    Rudà, Roberta; Trevisan, Elisa; Soffietti, Riccardo

    2010-11-01

    To present an overview of the recent findings in pathophysiology and management of epileptic seizures in patients with brain tumors. Low-grade gliomas are the most epileptogenic brain tumors. Regarding pathophysiology, the role of peritumoral changes [hypoxia and acidosis, blood-brain barrier (BBB) disruption, increase or decrease of neurotransmitters and receptors] are of increasing importance. Tumor-associated epilepsy and tumor growth could have some common molecular pathways. Total/subtotal surgical resection (with or without epilepsy surgery) allows a seizure control in a high percentage of patients. Radiotherapy and chemotherapy as well have a role. New antiepileptic drugs are promising, both in terms of efficacy and tolerability. The resistance to antiepileptic drugs is still a major problem: new insights into pathogenesis are needed to develop strategies to manipulate the pharmakoresistance. Epileptic seizures in brain tumors have been definitely recognized as one of the major problems in patients with brain tumors, and need specific and multidisciplinary approaches.

  5. Brain and Spinal Tumors

    MedlinePlus

    ... National Brain Tumor Society 55Chapel Street Suite 200 Newton MA Newton, MA 02458 questions@braintumor.org http://www.braintumor. ... National Brain Tumor Society 55Chapel Street Suite 200 Newton MA Newton, MA 02458 questions@braintumor.org http:// ...

  6. 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

  7. 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.

  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. Neonatal Brain Tumors: A Review

    PubMed Central

    Bodeliwala, Shaam; Kumar, Vikas; Singh, Daljit

    2017-01-01

    Brain tumors in neonatal age group is uncommon comparing with older children and adults. In older children brain tumors are commonly infratentorial, where as in neonates, they are supratentorial. Though extracranial tumors are commoner in neonates, brain tumors cause 5-20% deaths approximately. We are presenting a review on brain tumors in neonates. PMID:28770127

  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. Validation techniques for quantitative brain tumors measurements.

    PubMed

    Salman, Y; Assal, M; Badawi, A; Alian, S; -M El-Bayome, M

    2005-01-01

    Quantitative measurements of tumor volume becomes more realistic with the use of imaging- particularly specially when the tumor have non-ellipsoidal morphology, which remains subtle, irregular and difficult to assess by visual metric and clinical examination. The quantitative measurements depend strongly on the accuracy of the segmentation technique. The validity of brain tumor segmentation methods is an important issue in medical imaging because it has a direct impact on many applications such as surgical planning and quantitative measurements of tumor volume. Our goal was to examine two popular segmentation techniques seeded region growing and active contour "snakes" to be compared against experts' manual segmentations as the gold standard. We illustrated these methods on brain tumor volume cases using MR imaging modality.

  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. Epilepsy and brain tumors

    PubMed Central

    ENGLOT, DARIO J.; CHANG, EDWARD F.; VECHT, CHARLES J.

    2016-01-01

    Seizures are common in patients with brain tumors, and epilepsy can significantly impact patient quality of life. Therefore, a thorough understanding of rates and predictors of seizures, and the likelihood of seizure freedom after resection, is critical in the treatment of brain tumors. Among all tumor types, seizures are most common with glioneuronal tumors (70–80%), particularly in patients with frontotemporal or insular lesions. Seizures are also common in individuals with glioma, with the highest rates of epilepsy (60–75%) observed in patients with low-grade gliomas located in superficial cortical or insular regions. Approximately 20–50% of patients with meningioma and 20–35% of those with brain metastases also suffer from seizures. After tumor resection, approximately 60–90% are rendered seizure-free, with most favorable seizure outcomes seen in individuals with glioneuronal tumors. Gross total resection, earlier surgical therapy, and a lack of generalized seizures are common predictors of a favorable seizure outcome. With regard to anticonvulsant medication selection, evidence-based guidelines for the treatment of focal epilepsy should be followed, and individual patient factors should also be considered, including patient age, sex, organ dysfunction, comorbidity, or cotherapy. As concomitant chemotherapy commonly forms an essential part of glioma treatment, enzyme-inducing anticonvulsants should be avoided when possible. Seizure freedom is the ultimate goal in the treatment of brain tumor patients with epilepsy, given the adverse effects of seizures on quality of life. PMID:26948360

  14. Imaging of brain tumors.

    PubMed

    Chourmouzi, Danai; Papadopoulou, Elissabet; Marias, Kostantinos; Drevelegas, Antonios

    2014-10-01

    Neuroimaging plays a crucial role in diagnosis of brain tumors and in the decision-making process for therapy. Functional imaging techniques can reflect cellular density (diffusion imaging), capillary density (perfusion techniques), and tissue biochemistry (magnetic resonance [MR] spectroscopy). In addition, cortical activation imaging (functional MR imaging) can identify various loci of eloquent cerebral cortical function. Combining these new tools can increase diagnostic specificity and confidence. Familiarity with conventional and advanced imaging findings facilitates accurate diagnosis, differentiation from other processes, and optimal patient treatment. This article is a practical synopsis of pathologic, clinical, and imaging spectra of most common brain tumors. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. 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.

  16. 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

  17. Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering.

    PubMed

    Emblem, Kyrre E; Nedregaard, Baard; Hald, John K; Nome, Terje; Due-Tonnessen, Paulina; Bjornerud, Atle

    2009-07-01

    To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P = 0.576-0.970). When identifying a high-risk patient group (expected survival <2 years) and a low-risk patient group (expected survival >2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P < 0.001) compared with the manual volumes (10.650-12.761; P = 0.001-0.002). Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization. (c) 2009 Wiley-Liss, Inc.

  18. 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) ...

  19. 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

  20. 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.

  1. Neonatal brain MRI segmentation: A review.

    PubMed

    Devi, Chelli N; Chandrasekharan, Anupama; Sundararaman, V K; Alex, Zachariah C

    2015-09-01

    This review paper focuses on the neonatal brain segmentation algorithms in the literature. It provides an overview of clinical magnetic resonance imaging (MRI) of the newborn brain and the challenges in automated tissue classification of neonatal brain MRI. It presents a complete survey of the existing segmentation methods and their salient features. The different approaches are categorized into intracranial and brain tissue segmentation algorithms based on their level of tissue classification. Further, the brain tissue segmentation techniques are grouped based on their atlas usage into atlas-based, augmented atlas-based and atlas-free methods. In addition, the research gaps and lacunae in literature are also identified. Copyright © 2015 Elsevier Ltd. All rights reserved.

  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. 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.

  4. 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.

  5. Origins of Brain Tumor Macrophages.

    PubMed

    De Palma, Michele

    2016-12-12

    The ontogeny of brain-tumor-associated macrophages is poorly understood. New findings indicate that both resident microglia and blood-derived monocytes generate the pool of macrophages that infiltrate brain tumors of either primary or metastatic origin. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. 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

  7. Mechanism of brain tumor headache.

    PubMed

    Taylor, Lynne P

    2014-04-01

    Headaches occur commonly in all patients, including those who have brain tumors. Using the search terms "headache and brain tumors," "intracranial neoplasms and headache," "facial pain and brain tumors," "brain neoplasms/pathology," and "headache/etiology," we reviewed the literature from the past 78 years on the proposed mechanisms of brain tumor headache, beginning with the work of Penfield. Most of what we know about the mechanisms of brain tumor associated headache come from neurosurgical observations from intra-operative dural and blood vessel stimulation as well as intra-operative observations and anecdotal information about resolution of headache symptoms with various tumor-directed therapies. There is an increasing overlap between the primary and secondary headaches and they may actually share a similar biological mechanism. While there can be some criticism that the experimental work with dural and arterial stimulation produced head pain and not actual headache, when considered with the clinical observations about headache type, coupled with improvement after treatment of the primary tumor, we believe that traction on these structures, coupled with increased intracranial pressure, is clearly part of the genesis of brain tumor headache and may also involve peripheral sensitization with neurogenic inflammation as well as a component of central sensitization through trigeminovascular afferents on the meninges and cranial vessels. © 2014 American Headache Society.

  8. 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

  9. 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.

  10. Adolescent and Pediatric Brain Tumors

    MedlinePlus

    ... Children Pediatric Brain Tumor Diagnosis Family Impact Late Effects After Treatment Returning to School Pediatric Caregiver Resource Center About Us Our Founders Board of Directors Staff Leadership Strategic Plan Financials ...

  11. Brain tumor survivors speak out.

    PubMed

    Carlson-Green, Bonnie

    2009-01-01

    Although progress has been made in the treatment of childhood brain tumors,work remains to understand the complexities of disease, treatment, and contextual factors that underlie individual differences in outcome. A combination of both an idiographic approach (incorporating observations made by adult survivors of childhood brain tumors) and a nomothetic approach (reviewing the literature for brain tumor survivors as well as childhood cancer survivors) is presented. Six areas of concern are reviewed from both an idiographic and nomothetic perspective, including social/emotional adjustment, insurance, neurocognitive late effects, sexuality and relationships, employment, and where survivors accessed information about their disease and treatment and possible late effects. Guidelines to assist health care professionals working with childhood brain tumor survivors are offered with the goal of improving psychosocial and neurocognitive outcomes in this population.

  12. 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.

  13. Curcumin blocks brain tumor formation.

    PubMed

    Purkayastha, Sudarshana; Berliner, Alexandra; Fernando, Suraj Shawn; Ranasinghe, Buddima; Ray, Indrani; Tariq, Hussnain; Banerjee, Probal

    2009-04-17

    Turmeric, an essential ingredient of culinary preparations of Southeast Asia, contains a major polyphenolic compound, named curcumin or diferuloylmethane, which eliminates cancer cells derived from a variety of peripheral tissues. Although in vitro experiments have addressed its anti-tumor property, no in vivo studies have explored its anti-cancer activity in the brain. Oral delivery of this food component has been less effective because of its low solubility in water.We show that a soluble formulation of curcumin crosses the blood–brain barrier but does not suppress normal brain cell viability. Furthermore, tail vein injection, or more effectively, intracerebral injection through a cannula, blocks brain tumor formation in mice that had already received an intracerebral bolus of mouse melanoma cells (B16F10).While exploring the mechanism of its action in vitro we observed that the solubilized curcumin causes activation of proapoptotic enzymes caspase 3/7 in human oligodendroglioma (HOG) and lung carcinoma (A549) cells, and mouse tumor cells N18(neuroblastoma), GL261 (glioma), and B16F10. A simultaneous decrease in cell viability is also revealed by MTT [3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide]assays. Further examination of the B16F10 cells showed that curcumin effectively suppresses Cyclin D1, P-NF-kB, BclXL, P-Akt, and VEGF, which explains its efficacy in blocking proliferation, survival, and invasion of the B16F10 cells in the brain. Taken together,solubilized curcumin effectively blocks brain tumor formation and also eliminates brain tumor cells. Therefore, judicious application of such injectable formulations of curcumin could be developed into a safe therapeutic strategy for treating brain tumors.

  14. 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.

  15. [Brain tumor and headache.].

    PubMed

    Kiss, I; Franz, M; Kilian, M

    1994-09-01

    The possible association of brain tumour with headache was investigated in 100 patients seen for brain surgery. Preoperatively, 43 patients suffered from headache. These patients were thoroughly questioned about the nature of their pain. Investigation included the McGill Pain Questionnaire. In only 11 of the patients was headache the primary symptom of a brain tumour. Pain intensity was found to be lower in patients with brain tumour then in those with extracranial tumours or headache of other origins. Female subjects, patients under 50 years of age and those with elevated intracranial pressure experienced more intensive pain. Diurnal variation in pain intensity was observed in 60% of patients with headache. There was no evidence, however, of an association with elevated intracranial pressure. Our investigations yielded new information concerning the epidemology of headache accompanying brain tumours. Headache is not an early cardinal symptom of brain tumours, as was generally believed earlier. With the help of the McGill Pain Questionnaire a fine quantitative and qualitative characterization of headache of different origins could be made. The connection between tumour localization and pain lateralization, as well as the possible mechanisms of intracranial pain projection was extensively analysed. The interpretations of the results are at best hypotheses and they do not help determine why more than half of the patients with brain tumour did not experience headache.

  16. 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 ...

  17. 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.

  18. Brain Tumor Imaging.

    PubMed

    Brindle, Kevin M; Izquierdo-García, José L; Lewis, David Y; Mair, Richard J; Wright, Alan J

    2017-07-20

    Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.

  19. 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.

  20. [Brain tumors in nursing infants].

    PubMed

    Trujillo-Maldonado, A; Dávila-Gutiérrez, G; Escanero-Salazar, A; Paredes-Díaz, E; Alcalá-Negrete, H

    1991-11-01

    The purpose of this study was to determine the anatomical-pathological distribution of brain tumors in children under two years of age and their clinical characteristics (age, sex, time span from the start of symptoms or signs to the time the tumor was diagnosed, main clinical manifestations, evolution and prognosis). From 1981 to 1989, 16 children with brain tumors, under two years of age, were studied. The tumors arose in 13 patients during first year of life and during the second, in the remaining three. In 50% of the patients, the tumors were supratentorial. The histological diagnosis was made in all cases, finding the ependymoma the most frequent tumor, followed by the astrocytoma and other tumors: teratoma, choroid plexi papilloma. The increase in size was within the cephalic perimeter, with a risen fontanelle, irritability, vomiting and convulsive episodes, as main clinical manifestations. In 15 of the patients a partial or total resection of the tumor was performed, 6 were given radiotherapy and 2 chemotherapy. The prognosis correlated with the greatest surgical risk, the anatomical-pathological characteristics and the lateness in its diagnosis. We emphasize the greater morbi-mortality rate with respect to other pediatric ages.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Evaluation of Atlas based Mouse Brain Segmentation

    PubMed Central

    Lee, Joohwi; Jomier, Julien; Aylward, Stephen; Tyszka, Mike; Moy, Sheryl; Lauder, Jean; Styner, Martin

    2010-01-01

    Magentic Reasonance Imaging for mouse phenotype study is one of the important tools to understand human diseases. In this paper, we present a fully automatic pipeline for the process of morphometric mouse brain analysis. The method is based on atlas-based tissue and regional segmentation, which was originally developed for the human brain. To evaluate our method, we conduct a qualitative and quantitative validation study as well as compare of b-spline and fluid registration methods as components in the pipeline. The validation study includes visual inspection, shape and volumetric measurements and stability of the registration methods against various parameter settings in the processing pipeline. The result shows both fluid and b-spline registration methods work well in murine settings, but the fluid registration is more stable. Additionally, we evaluated our segmentation methods by comparing volume differences between Fmr1 FXS in FVB background vs C57BL/6J mouse strains. PMID:20640188

  6. 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

  7. Toward real-time tumor margin identification in image-guided robotic brain tumor resection

    NASA Astrophysics Data System (ADS)

    Hu, Danying; Jiang, Yang; Belykh, Evgenii; Gong, Yuanzheng; Preul, Mark C.; Hannaford, Blake; Seibel, Eric J.

    2017-03-01

    For patients with malignant brain tumors (glioblastomas), a safe maximal resection of tumor is critical for an increased survival rate. However, complete resection of the cancer is hard to achieve due to the invasive nature of these tumors, where the margins of the tumors become blurred from frank tumor to more normal brain tissue, but in which single cells or clusters of malignant cells may have invaded. Recent developments in fluorescence imaging techniques have shown great potential for improved surgical outcomes by providing surgeons intraoperative contrast-enhanced visual information of tumor in neurosurgery. The current near-infrared (NIR) fluorophores, such as indocyanine green (ICG), cyanine5.5 (Cy5.5), 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX), are showing clinical potential to be useful in targeting and guiding resections of such tumors. Real-time tumor margin identification in NIR imaging could be helpful to both surgeons and patients by reducing the operation time and space required by other imaging modalities such as intraoperative MRI, and has the potential to integrate with robotically assisted surgery. In this paper, a segmentation method based on the Chan-Vese model was developed for identifying the tumor boundaries in an ex-vivo mouse brain from relatively noisy fluorescence images acquired by a multimodal scanning fiber endoscope (mmSFE). Tumor contours were achieved iteratively by minimizing an energy function formed by a level set function and the segmentation model. Quantitative segmentation metrics based on tumor-to-background (T/B) ratio were evaluated. Results demonstrated feasibility in detecting the brain tumor margins at quasi-real-time and has the potential to yield improved precision brain tumor resection techniques or even robotic interventions in the future.

  8. 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 ...

  9. 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.

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

    ClinicalTrials.gov

    2016-05-13

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

  11. Deregulated proliferation and differentiation in brain tumors

    PubMed Central

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

    2014-01-01

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

  12. 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.

  13. Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth

    PubMed Central

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

    2009-01-01

    Although a variety of diffeomorphic deformable registration methods exist in the literature, application of these methods in the presence of space-occupying lesions is not straightforward. The motivation of this work is spatial normalization of MR images from patients with brain tumors in a common stereotaxic space, aiming to pool data from different patients into a common space in order to perform group analyses. Additionally, transfer of structural and functional information from neuroanatomical brain atlases into the individual patient's space can be achieved via the inverse mapping, for the purpose of segmenting brains and facilitating surgical or radiotherapy treatment planning. A method that estimates the brain tissue loss and replacement by tumor is applied for achieving equivalent image content between an atlas and a patient's scan, based on a biomechanical model of tumor growth. Automated estimation of the parameters modeling brain tissue loss and displacement is performed via optimization of an objective function reflecting feature-based similarity and elastic stretching energy, which is optimized in parallel via APPSPACK (Asynchronous Parallel Pattern Search). The results of the method, applied to 21 brain tumor patients, indicate that the registration accuracy is relatively high in areas around the tumor, as well as in the healthy portion of the brain. Also, the calculated deformation in the vicinity of the tumor is shown to correlate highly with expert-defined visual scores indicating the tumor mass effect, thereby potentially leading to an objective approach to quantification of mass effect, which is commonly used in diagnosis. PMID:19408350

  14. 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.

  15. What You Need to Know about Brain Tumors

    MedlinePlus

    ... in the brain. These tumors are called primary brain tumors. Cancer that spreads to the brain from another part ... covers: How brain tumors are diagnosed Treatments for brain tumors, including taking part in cancer treatment research studies Problems that brain tumors might ...

  16. 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.

  17. 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 ...

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Engineering challenges for brain tumor immunotherapy.

    PubMed

    Lyon, Johnathan G; Mokarram, Nassir; Saxena, Tarun; Carroll, Sheridan L; Bellamkonda, Ravi V

    2017-05-15

    Malignant brain tumors represent one of the most devastating forms of cancer with abject survival rates that have not changed in the past 60years. This is partly because the brain is a critical organ, and poses unique anatomical, physiological, and immunological barriers. The unique interplay of these barriers also provides an opportunity for creative engineering solutions. Cancer immunotherapy, a means of harnessing the host immune system for anti-tumor efficacy, is becoming a standard approach for treating many cancers. However, its use in brain tumors is not widespread. This review discusses the current approaches, and hurdles to these approaches in treating brain tumors, with a focus on immunotherapies. We identify critical barriers to immunoengineering brain tumor therapies and discuss possible solutions to these challenges. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  5. Dynamic perfusion CT in brain tumors.

    PubMed

    Yeung, Timothy Pok Chi; Bauman, Glenn; Yartsev, Slav; Fainardi, Enrico; Macdonald, David; Lee, Ting-Yim

    2015-12-01

    Dynamic perfusion CT (PCT) is an imaging technique for assessing the vascular supply and hemodynamics of brain tumors by measuring blood flow, blood volume, and permeability-surface area product. These PCT parameters provide information complementary to histopathologic assessments and have been used for grading brain tumors, distinguishing high-grade gliomas from other brain lesions, differentiating true progression from post-treatment effects, and predicting prognosis after treatments. In this review, the basic principles of PCT are described, and applications of PCT of brain tumors are discussed. The advantages and current challenges, along with possible solutions, of PCT are presented. Copyright © 2015. Published by Elsevier Ireland Ltd.

  6. 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

  7. 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

  8. [Brain tumors in patients primarly treated psychiatrically].

    PubMed

    Ristić, Dragana Ignjatović; Vesna, Pusicić; Sanja, Pejović; Dejanović, Slavica Djukić; Milovanović, Dragan R; Ravanić, Dragan B; Vladimir, Janjić

    2011-09-01

    Psychiatric symptoms are not rare manifestations of brain tumors. Brain tumors presented by symptoms of raised intracranial pressure, focal neurological signs, or convulsions are usually first seen by the neurologist or less frequently by the neurosurgeon in routine diagnostic procedures. On the other hand, when psychiatric symptoms are the first manifestation in "neurologically silent" brain tumors, the patients are sent to the psychiatrist for the treatment of psychiatric symptoms and brain tumors are left misdiagnosed for a long period of time. We presented three patients with the diagnosed brain tumor where psychiatrist had been the first specialist to be consulted. In all three cases neurological examination was generally unremarkable with no focal signs or features of raised intracranial pressure. CT scan demonstrated right insular tumor in a female patient with obsessive-compulsive disorder (OCD); right parietal temporal tumor in a patient with delusions and depression and left frontal tumor in a patient with history of alcohol dependency. Psychiatric symptoms/disorders in patients with brain tumors are not specific enough and can have the same clinical presentation as the genuine psychiatric disorder. Therefore, we emphasize the consideration of neuroimaging in patients with abrupt beginning of psychiatric symptoms, in those with a change in mental status, or when headaches suddenly appear or in cases of treatment resistant psychiatric disorders regardless the lack of neurological symptoms.

  9. [Features of brain stem tumors in children].

    PubMed

    Ciobanu, Antonela; Miron, Ingrith; Tansanu, I

    2012-01-01

    Brain stem tumors account for about 10-20% of childhood brain tumors. Peak incidence for these tumors occurs around age 6 to 7 years. Despite their severity and poor prognosis, brain stem tumors remain an area of intense research with regard to their diagnosis and management. In the interval 2003-2010, 8 children (4 girls and 4 boys) aged 2-13 years (mean age 6.82), diagnosed with brain stem tumors were followed up. Disease history, onset symptoms, complete physical, laboratory and imaging investigations, and individualized therapeutic approach have been reviewed. Family history was considered to be of particular clinical importance. Monitoring the disease progression was possible until the time of death (when it occurred in hospital) or by information provided by the family and family physician in cases where death occurred at patient's home. Clinical signs and symptoms depend on tumor location, its aggressiveness, and patient's age. Progressive neurological deficits, signs and symptoms caused by increased intracranial pressure, visual disturbances, behavioral disorders, seizures, endocrine disruption, failure to thrive may occur in various combinations. In only 50% of our cases the tumor could be removed. Imaging proved highly suggestive for a brain stem tumor. Histopathological examination diagnosed one pilocytic astrocytoma (grade I), one fibrillary astrocytoma (grade II), one anaplastic astrocytoma (grade III), and one glioblastoma multiforme (grade IV). In the remaining 4 cases imaging was suggestive for glial tumors. Multimodal therapy was used in 2 patients, 7 received adjuvant chemotherapy, and in 1 case no therapy was administered because the tumor rapidly progressed to death. Seven of our patients died on an average of 6.28 months after the diagnosis (range 2 to 9 months). A family history of brain tumors in 2 of our cases supports the hypothesis of genetic factors involvement. Brain stem tumors are still difficult to investigate, and the results on

  10. 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

  11. 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.

  12. 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

  13. 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.

  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. 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.

  17. 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".

  18. 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.

  19. 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.

  20. 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

  1. 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.

  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. LOCUS: LOcal Cooperative Unified Segmentation of MRI brain scans

    PubMed Central

    Scherrer, Benoît; Dojat, Michel; Forbes, Florence; Garbay, Catherine

    2007-01-01

    We propose to carry out cooperatively both tissue and structure segmentations by distributing a set of local and cooperative models in a unified MRF framework. Tissue segmentation is performed by partitionning the volume into subvolumes where local MRFs are estimated in cooperation with their neighbors to ensure consistency. Local estimation fits precisely to the local intensity distribution and thus handles nonuniformity of intensity without any bias field modelization. Structure segmentation is performed via local MRFs that integrate localization constraints provided by a priori general fuzzy description of brain anatomy. Structure segmentation is not reduced to a postprocessing step but cooperates with tissue segmentation to gradually and conjointly improve models accuracy. The evaluation was performed using phantoms and real 3T brain scans. It shows good results and in particular robustness to nonuniformity and noise with a low computational cost. PMID:18051062

  4. 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 ...

  5. 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.

  6. Work productivity in brain tumor survivors.

    PubMed

    Feuerstein, Michael; Hansen, Jennifer A; Calvio, Lisseth C; Johnson, Leigh; Ronquillo, Jonne G

    2007-07-01

    To determine the association of symptom burden to work limitation among working survivors of malignant brain tumors. Working adults with malignant brain tumors (n = 95) and a non-cancer comparison (n = 131) group completed a web-based questionnaire. Measures of demographics, tumor type and treatment, fatigue, emotional distress, cognitive limitations, and factors that can positively impact work, including health behaviors and problem solving, were obtained. Survivors of malignant brain tumors reported higher levels of work limitations and time off from work than the non-cancer group. Higher levels of symptom burden, lower levels of health behaviors, and more negative problem solving orientation were characteristic of the brain tumor survivor group. These variables were not differentially associated with work limitations among brain cancer survivors or the comparison group. Depressive symptoms, fatigue, cognitive limitations, sleep, and negative problem solving orientation were independently associated with work limitations, accounting for 65% of the variance in work limitations. Despite higher levels of burden, poorer health behaviors, and negative problem solving coping style, modifiable factors account for most of the variance in work limitations for both groups. Efforts to modify these variables should be evaluated.

  7. 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.

  8. 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.

  9. 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.

  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. 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.

  12. Monitoring therapeutic monoclonal antibodies in brain tumor

    PubMed Central

    Ait-Belkacem, Rima; Berenguer, Caroline; Villard, Claude; Ouafik, L’Houcine; Figarella-Branger, Dominique; Beck, Alain; Chinot, Olivier; Lafitte, Daniel

    2014-01-01

    Bevacizumab induces normalization of abnormal blood vessels, making them less leaky. By binding to vascular endothelial growth factor, it indirectly attacks the vascular tumor mass. The optimal delivery of targeted therapies including monoclonal antibodies or anti-angiogenesis drugs to the target tissue highly depends on the blood-brain barrier permeability. It is therefore critical to investigate how drugs effectively reach the tumor. In situ investigation of drug distribution could provide a better understanding of pharmacological agent action and optimize chemotherapies for solid tumors. We developed an imaging method coupled to protein identification using matrix-assisted laser desorption/ionization mass spectrometry. This approach monitored bevacizumab distribution within the brain structures, and especially within the tumor, without any labeling. PMID:25484065

  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. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Morphological Characteristics of Brain Tumors Causing Seizures

    PubMed Central

    Lee, Jong Woo; Wen, Patrick Y.; Hurwitz, Shelley; Black, Peter; Kesari, Santosh; Drappatz, Jan; Golby, Alexandra J.; Wells, William M.; Warfield, Simon K.; Kikinis, Ron; Bromfield, Edward B.

    2010-01-01

    Objective To quantify size and localization differences between tumors presenting with seizures vs nonseizure neurological symptoms. Design Retrospective imaging survey. We performed magnetic resonance imaging–based morphometric analysis and nonparametric mapping in patients with brain tumors. Setting University-affiliated teaching hospital. Patients or Other Participants One hundred twenty-four patients with newly diagnosed supratentorial glial tumors. Main Outcome Measures Volumetric and mapping methods were used to evaluate differences in size and location of the tumors in patients who presented with seizures as compared with patients who presented with other symptoms. Results In high-grade gliomas, tumors presenting with seizures were smaller than tumors presenting with other neurological symptoms, whereas in low-grade gliomas, tumors presenting with seizures were larger. Tumor location maps revealed that in high-grade gliomas, deep-seated tumors in the pericallosal regions were more likely to present with nonseizure neurological symptoms. In low-grade gliomas, tumors of the temporal lobe as well as the insular region were more likely to present with seizures. Conclusions The influence of size and location of the tumors on their propensity to cause seizures varies with the grade of the tumor. In high-grade gliomas, rapidly growing tumors, particularly those situated in deeper structures, present with non–seizure-related symptoms. In low-grade gliomas, lesions in the temporal lobe or the insula grow large without other symptoms and eventually cause seizures. Quantitative image analysis allows for the mapping of regions in each group that are more or less susceptible to seizures. PMID:20212231

  19. 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

  20. 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.

  1. 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

  2. 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).

  3. Homeomorphic Brain Image Segmentation with Topological and Statistical Atlases

    PubMed Central

    Bazin, Pierre-Louis; Pham, Dzung L.

    2008-01-01

    Atlas-based segmentation techniques are often employed to encode anatomical information for the delineation of multiple structures in magnetic resonance images of the brain. One of the primary challenges of these approaches is to efficiently model qualitative and quantitative anatomical knowledge without introducing a strong bias toward certain anatomical preferences when segmenting new images. This paper explores the use of topological information as a prior and proposes a segmentation framework based on both topological and statistical atlases of brain anatomy. Topology can be used to describe continuity of structures, as well as the relationships between structures, and is often a critical component in cortical surface reconstruction and deformation-based morphometry. Our method guarantees strict topological equivalence between the segmented image and the atlas, and relies only weakly on a statistical atlas of shape. Tissue classification and fast marching methods are used to provide a powerful and flexible framework to handle multiple image contrasts, high levels of noise, gain field inhomogeneities, and variable anatomies. The segmentation algorithm has been validated on simulated and real brain image data and made freely available to researchers. Our experiments demonstrate the accuracy and robustness of the method and the limited influence of the statistical atlas. PMID:18640069

  4. 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.

  5. Concurrent Tumor Segmentation and Registration with Uncertainty-based Sparse non-Uniform Graphs

    PubMed Central

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

    2014-01-01

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

  6. Automatic segmentation of MR images of the developing newborn brain.

    PubMed

    Prastawa, Marcel; Gilmore, John H; Lin, Weili; Gerig, Guido

    2005-10-01

    This paper describes an automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI). The analysis and study of newborn brain MRI is of great interest due to its potential for studying early growth patterns and morphological changes in neurodevelopmental disorders. Automatic segmentation of newborn MRI is a challenging task mainly due to the low intensity contrast and the growth process of the white matter tissue. Newborn white matter tissue undergoes a rapid myelination process, where the nerves are covered in myelin sheathes. It is necessary to identify the white matter tissue as myelinated or non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas. The method first uses robust graph clustering and parameter estimation to find the initial intensity distributions. The distribution estimates are then used together with the spatial priors to perform bias correction. Finally, the method refines the segmentation using training sample pruning and non-parametric kernel density estimation. Our results demonstrate that the method is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.

  7. 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.

  8. 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.

  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. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. Targeted toxins in brain tumor therapy.

    PubMed

    Li, Yan Michael; Hall, Walter A

    2010-11-01

    Targeted toxins, also known as immunotoxins or cytotoxins, are recombinant molecules that specifically bind to cell surface receptors that are overexpressed in cancer and the toxin component kills the cell. These recombinant proteins consist of a specific antibody or ligand coupled to a protein toxin. The targeted toxins bind to a surface antigen or receptor overexpressed in tumors, such as the epidermal growth factor receptor or interleukin-13 receptor. The toxin part of the molecule in all clinically used toxins is modified from bacterial or plant toxins, fused to an antibody or carrier ligand. Targeted toxins are very effective against cancer cells resistant to radiation and chemotherapy. They are far more potent than any known chemotherapy drug. Targeted toxins have shown an acceptable profile of toxicity and safety in early clinical studies and have demonstrated evidence of a tumor response. Currently, clinical trials with some targeted toxins are complete and the final results are pending. This review summarizes the characteristics of targeted toxins and the key findings of the important clinical studies with targeted toxins in malignant brain tumor patients. Obstacles to successful treatment of malignant brain tumors include poor penetration into tumor masses, the immune response to the toxin component and cancer heterogeneity. Strategies to overcome these limitations are being pursued in the current generation of targeted toxins.

  16. 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.

  17. 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.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  20. 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

  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. 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.

  3. 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

  4. 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) ...

  5. 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.

  6. 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.

  7. 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.

  8. [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.

  9. Novel whole brain segmentation and volume estimation using quantitative MRI.

    PubMed

    West, J; Warntjes, J B M; Lundberg, P

    2012-05-01

    Brain segmentation and volume estimation of grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) are important for many neurological applications. Volumetric changes are observed in multiple sclerosis (MS), Alzheimer's disease and dementia, and in normal aging. A novel method is presented to segment brain tissue based on quantitative magnetic resonance imaging (qMRI) of the longitudinal relaxation rate R(1), the transverse relaxation rate R(2) and the proton density, PD. Previously reported qMRI values for WM, GM and CSF were used to define tissues and a Bloch simulation performed to investigate R(1), R(2) and PD for tissue mixtures in the presence of noise. Based on the simulations a lookup grid was constructed to relate tissue partial volume to the R(1)-R(2)-PD space. The method was validated in 10 healthy subjects. MRI data were acquired using six resolutions and three geometries. Repeatability for different resolutions was 3.2% for WM, 3.2% for GM, 1.0% for CSF and 2.2% for total brain volume. Repeatability for different geometries was 8.5% for WM, 9.4% for GM, 2.4% for CSF and 2.4% for total brain volume. We propose a new robust qMRI-based approach which we demonstrate in a patient with MS. • A method for segmenting the brain and estimating tissue volume is presented • This method measures white matter, grey matter, cerebrospinal fluid and remaining tissue • The method calculates tissue fractions in voxel, thus accounting for partial volume • Repeatability was 2.2% for total brain volume with imaging resolution <2.0 mm.

  10. Targeting Malignant Brain Tumors with Antibodies.

    PubMed

    Razpotnik, Rok; Novak, Neža; Čurin Šerbec, Vladka; Rajcevic, Uros

    2017-01-01

    Antibodies have been shown to be a potent therapeutic tool. However, their use for targeting brain diseases, including neurodegenerative diseases and brain cancers, has been limited, particularly because the blood-brain barrier (BBB) makes brain tissue hard to access by conventional antibody-targeting strategies. In this review, we summarize new antibody therapeutic approaches to target brain tumors, especially malignant gliomas, as well as their potential drawbacks. Many different brain delivery platforms for antibodies have been studied such as liposomes, nanoparticle-based systems, cell-penetrating peptides (CPPs), and cell-based approaches. We have already shown the successful delivery of single-chain fragment variable (scFv) with CPP as a linker between two variable domains in the brain. Antibodies normally face poor penetration through the BBB, with some variants sufficiently passing the barrier on their own. A "Trojan horse" method allows passage of biomolecules, such as antibodies, through the BBB by receptor-mediated transcytosis (RMT). Such examples of therapeutic antibodies are the bispecific antibodies where one binding specificity recognizes and binds a BBB receptor, enabling RMT and where a second binding specificity recognizes an antigen as a therapeutic target. On the other hand, cell-based systems such as stem cells (SCs) are a promising delivery system because of their tumor tropism and ability to cross the BBB. Genetically engineered SCs can be used in gene therapy, where they express anti-tumor drugs, including antibodies. Different types and sources of SCs have been studied for the delivery of therapeutics to the brain; both mesenchymal stem cells (MSCs) and neural stem cells (NSCs) show great potential. Following the success in treatment of leukemias and lymphomas, the adoptive T-cell therapies, especially the chimeric antigen receptor-T cells (CAR-Ts), are making their way into glioma treatment as another type of cell-based therapy using the

  11. Neurological outcome of childhood brain tumor survivors.

    PubMed

    Pietilä, Sari; Korpela, Raija; Lenko, Hanna L; Haapasalo, Hannu; Alalantela, Riitta; Nieminen, Pirkko; Koivisto, Anna-Maija; Mäkipernaa, Anne

    2012-05-01

    We assessed neurological and neurocognitive outcome in childhood brain tumor survivors. Altogether, 75 out of 80 brain tumor survivors diagnosed below 17 years between 1983 and 1997; and treated in Tampere University Hospital, Finland, were invited to participate in this population-based cross-sectional study. Fifty-two (69%) participated [mean age 14.2 (3.8-28.7) years, mean follow-up 7.5 (1.5-15.1) years]. Neurological status was abnormal in 69% cases. All were ambulatory, but only 50% showed normal motor function. Twenty-nine percent showed clumsiness/mild asymmetry and 21% hemiparesis. One suffered from intractable epilepsy. According to structured interview, 87% coped normally in daily living. Median full-scale IQ was 85 (39-110) in 21 6-16 year olds (70%); in 29% IQ was <70. Thirty of the 44 school-aged subjects attended school with normal syllabus and 32% needed special education. Six of the 16 patients over 18 years of age were working. Regarding quality of life, 38% were active without disability, 33% active with mild disability, 21% were partially disabled, but capable of self-care, and 8% had severe disability, being incapable of self-care. Supratentorial/hemispheric tumor location, tumor reoperations, shunt revisions and chemotherapy were associated with neurological, cognitive and social disabilities. In conclusion, of the 52 survivors, neurological status was abnormal in 69%; 71% lived an active life with minor disabilities, 29% had major neurological, cognitive and social disabilities, and 8% of them were incapable of self-care. Predictors of these disabilities included supratentorial/hemispheric tumor location, tumor reoperations, shunt revisions and chemotherapy. Survivors need life-long, tailor-made multiprofessional support and follow-up.

  12. A fully automated algorithm under modified FCM framework for improved brain MR image segmentation.

    PubMed

    Sikka, Karan; Sinha, Nitesh; Singh, Pankaj K; Mishra, Amit K

    2009-09-01

    Automated brain magnetic resonance image (MRI) segmentation is a complex problem especially if accompanied by quality depreciating factors such as intensity inhomogeneity and noise. This article presents a new algorithm for automated segmentation of both normal and diseased brain MRI. An entropy driven homomorphic filtering technique has been employed in this work to remove the bias field. The initial cluster centers are estimated using a proposed algorithm called histogram-based local peak merger using adaptive window. Subsequently, a modified fuzzy c-mean (MFCM) technique using the neighborhood pixel considerations is applied. Finally, a new technique called neighborhood-based membership ambiguity correction (NMAC) has been used for smoothing the boundaries between different tissue classes as well as to remove small pixel level noise, which appear as misclassified pixels even after the MFCM approach. NMAC leads to much sharper boundaries between tissues and, hence, has been found to be highly effective in prominently estimating the tissue and tumor areas in a brain MR scan. The algorithm has been validated against MFCM and FMRIB software library using MRI scans from BrainWeb. Superior results to those achieved with MFCM technique have been observed along with the collateral advantages of fully automatic segmentation, faster computation and faster convergence of the objective function.

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

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. Deep learning for brain tumor classification

    NASA Astrophysics Data System (ADS)

    Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

    2017-03-01

    Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

  5. Targeting Nanomedicine to Brain Tumors: Latest Progress and Achievements.

    PubMed

    Van't Root, Moniek; Lowik, Clemens; Mezzanotte, Laura

    2017-01-01

    Targeting nanomedicine to brain tumors is hampered by the heterogeneity of brain tumors and the blood brain barrier. These represent the main reasons of unsuccessful treatments. Nanomedicine based approaches hold promise for improved brain tissue distribution of drugs and delivery of combination therapies. In this review, we describe the recent advancements and latest achievements in the use of nanocarriers, virus and cell-derived nanoparticles for targeted therapy of brain tumors. We provide successful examples of nanomedicine based approaches for direct targeting of receptors expressed in brain tumor cells or modulation of pathways involved in cell survival as well as approaches for indirect targeting of cells in the tumor stroma and immunotherapies. Although the field is at its infancy, clinical trials involving nanomedicine based approaches for brain tumors are ongoing and many others will start in the near future. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed

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

    2015-01-01

    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. 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. 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. 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.

  9. 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

  10. 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

  11. 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.

  12. 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)

  13. Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

    NASA Astrophysics Data System (ADS)

    Gaffney, Kevin P.; Aghaei, Faranak; Battiste, James; Zheng, Bin

    2017-03-01

    Detection of residual brain tumor is important to evaluate efficacy of brain cancer surgery, determine optimal strategy of further radiation therapy if needed, and assess ultimate prognosis of the patients. Brain MR is a commonly used imaging modality for this task. In order to distinguish between residual tumor and surgery induced scar tissues, two sets of MRI scans are conducted pre- and post-gadolinium contrast injection. The residual tumors are only enhanced in the post-contrast injection images. However, subjective reading and quantifying this type of brain MR images faces difficulty in detecting real residual tumor regions and measuring total volume of the residual tumor. In order to help solve this clinical difficulty, we developed and tested a new interactive computer-aided detection scheme, which consists of three consecutive image processing steps namely, 1) segmentation of the intracranial region, 2) image registration and subtraction, 3) tumor segmentation and refinement. The scheme also includes a specially designed and implemented graphical user interface (GUI) platform. When using this scheme, two sets of pre- and post-contrast injection images are first automatically processed to detect and quantify residual tumor volume. Then, a user can visually examine segmentation results and conveniently guide the scheme to correct any detection or segmentation errors if needed. The scheme has been repeatedly tested using five cases. Due to the observed high performance and robustness of the testing results, the scheme is currently ready for conducting clinical studies and helping clinicians investigate the association between this quantitative image marker and outcome of patients.

  14. 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

  15. 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.

  16. A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data

    NASA Astrophysics Data System (ADS)

    Goetz, Michael; Heim, Eric; Maerz, Keno; Norajitra, Tobias; Hafezi, Mohammadreza; Fard, Nassim; Mehrabi, Arianeb; Knoll, Max; Weber, Christian; Maier-Hein, Lena; Maier-Hein, Klaus H.

    2016-03-01

    Current fully automatic liver tumor segmentation systems are designed to work on a single CT-image. This hinders these systems from the detection of more complex types of liver tumor. We therefore present a new algorithm for liver tumor segmentation that allows incorporating different CT scans and requires no manual interaction. We derive a liver segmentation with state-of-the-art shape models which are robust to initialization. The tumor segmentation is then achieved by classifying all voxels into healthy or tumorous tissue using Extremely Randomized Trees with an auto-context learning scheme. Using DALSA enables us to learn from only sparse annotations and allows a fast set-up for new image settings. We validate the quality of our algorithm with exemplary segmentation results.

  17. 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.

  18. 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.

  19. RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI.

    PubMed

    Oguz, Ipek; Zhang, Honghai; Rumple, Ashley; Sonka, Milan

    2014-01-15

    High-field MRI is a popular technique for the study of rodent brains. These datasets, while similar to human brain MRI in many aspects, present unique image processing challenges. We address a very common preprocessing step, skull-stripping, which refers to the segmentation of the brain tissue from the image for further processing. While several methods exist for addressing this problem, they are computationally expensive and often require interactive post-processing by an expert to clean up poorly segmented areas. This further increases total processing time per subject. We propose a novel algorithm, based on grayscale mathematical morphology and LOGISMOS-based graph segmentation, which is rapid, robust and highly accurate. Comparative results obtained on two challenging in vivo datasets, consisting of 22 T1-weighted rat brain images and 10 T2-weighted mouse brain images illustrate the robustness and excellent performance of the proposed algorithm, in a fraction of the computational time needed by existing algorithms. In comparison to current state-of-the-art methods, our approach achieved average Dice similarity coefficient of 0.92 ± 0.02 and average Hausdorff distance of 13.6 ± 5.2 voxels (vs. 0.85 ± 0.20, p<0.05 and 42.6 ± 22.9, p < 0.001) for the rat dataset, and 0.96 ± 0.01 and average Hausdorff distance of 21.6 ± 12.7 voxels (vs. 0.93 ± 0.01, p <0.001 and 33.7 ± 3.5, p <0.001) for the mouse dataset. The proposed algorithm took approximately 90s per subject, compared to 10-20 min for the neural-network based method and 30-90 min for the atlas-based method. RATS is a robust and computationally efficient method for accurate rodent brain skull-stripping even in challenging data. Copyright © 2013 Elsevier B.V. All rights reserved.

  20. 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.

  1. What Are Brain and Spinal Cord Tumors in Children?

    MedlinePlus

    ... cells in the brain. They transmit chemical and electric signals that determine thought, memory, emotion, speech, muscle ... brain and spinal cord. This helps neurons send electric signals through the axons. Tumors starting in these ...

  2. 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.

  3. Pineal calcification is associated with pediatric primary brain tumor.

    PubMed

    Tuntapakul, Supinya; Kitkhuandee, Amnat; Kanpittaya, Jaturat; Johns, Jeffrey; Johns, Nutjaree Pratheepawanit

    2016-12-01

    Melatonin has been associated with various tumors, including brain tumor, and shown to inhibit growth of neuroblastoma cells and gliomas in animal models. Likewise, patients with glioblastoma receiving melatonin reported better survival than controls. Pineal calcification may lead to a decreased production of melatonin by calcified glands. This study assessed association between pineal calcification and primary brain tumor in pediatric/adolescent patients. Medical chart review was conducted in 181 patients <15 years old who had undergone brain computed tomography (CT) during 2008-2012. Pineal calcification was identified using brain CT scan by an experienced neurosurgeon. Primary brain tumor was confirmed by CT scan and histology, and association with pineal calcification was estimated using multiple logistic regression, adjusted for age and gender. Primary brain tumor was detected in 51 patients (mean age 9.0, standard deviation 4.0 years), with medulloblastoma being the most common (11 patients). Pineal calcification was detected in 12 patients (23.5%) with primary brain tumor, while only 11 patients (8.5%) without tumor had pineal calcification. Adjusted for patients' ages and genders, pineal calcification was associated with an increase in primary brain tumor of 2.82-fold (odds ratio 2.82; 95% confidence interval 1.12-7.08, P = 0.027). Pineal calcification appears to be associated with primary brain tumor. Further studies to explore this link are discussed and warranted. © 2016 John Wiley & Sons Australia, Ltd.

  4. Cortical dysplasia: a possible substrate for brain tumors

    PubMed Central

    Liu, Shiyong; Zhang, Chunqing; Shu, Haifeng; Wion, Didier; Yang, Hui

    2012-01-01

    The similarities between brain tumor stem cells and neural stem cells suggest a possible stem cell origin of tumorigenesis. Recently, cells with features of stem cells have been observed in lesions of adult and pediatric cortical dysplasia (CD). Given the evidence for a close relationship between CD and certain brain tumors, together with the finding that CD neural stem cells/progenitors are abnormally developed, we propose that CD is a possible substrate for brain tumors. The neural stem cells/progenitors in CD have accumulating abnormalities, and these abnormal stem/progenitor cells may be the initiating, transformed cells of brain tumors, when subsequently exposed to a carcinogen. PMID:22409462

  5. Photodynamic therapy for implanted VX2 tumor in rabbit brains

    NASA Astrophysics Data System (ADS)

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

    2005-07-01

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

  6. Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

    PubMed Central

    Gousias, Ioannis S.; Hammers, Alexander; Counsell, Serena J.; Srinivasan, Latha; Rutherford, Mary A.; Heckemann, Rolf A.; Hajnal, Jo V.; Rueckert, Daniel; Edwards, A. David

    2013-01-01

    We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain. PMID:23565180

  7. 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

  8. Cathepsin D and its prognostic value in neuroepithelial brain tumors.

    PubMed

    Pigac, Biserka; Dmitrović, Branko; Marić, Svjetlana; Masić, Silvija

    2012-03-01

    Expression of Cathepsin D (Cath D) in some primary neuroepithelial brain tumors and its prognostic value were studied. The research included 65 samples of human primary neuroepithelial brain tumors. There were 50 glial tumors (10 diffuse astrocytomas (DA), 15 anaplastic astrocytomas (AA), 25 glioblastomas (GB), 15 embryonic tumors (15 medulloblastomas (MB) as well as 5 samples of normal brain tissue. Immunohistochemical method was applied to monitor diffuse positive reaction in the cytoplasm of brain tumor cells, endothelial cells and tumor stromal cells and showed diffuse positive reaction for Cath D in the cytoplasm of brain tumor cells, endothelial cells and stromal cells in all analyzed samples of DA, AA, GB and MB as well as in microglial cells, neurons and in endothelial cells in all analyzed samples of normal brain tissue. Qualitative analysis of Cath D expression in the cytoplasm of brain tumor cells and endothelial cells as well as the percentage of brain tumor cells, endothelial cells and stromal cells immunopositive for Cath D showed that there was difference between analyzed brain tumor groups, but according to statistical tests the difference was not statistically significant. Survival correlated with the percentage of stromal cells immunopositive for Cath D. Survival prognosis was influenced by the percentage of stromal cells immunopositive for Cath D and tumor grade. The obtained results singled out the percentage of stromal cells immunopositive for Cath D as an independent parameter. The results of this research on the prognostic value of Cath D in some primary brain tumors of neuroepithelial origin indicate that there is real possibility to use Cath D as an independent prognostic factor in human glioma progression and thus open up possibilities for further scientific research.

  9. Vibrational Profiling of Brain Tumors and Cells.

    PubMed

    Nelson, Sultan L; Proctor, Dustin T; Ghasemloonia, Ahmad; Lama, Sanju; Zareinia, Kourosh; Ahn, Younghee; Al-Saiedy, Mustafa R; Green, Francis Hy; Amrein, Matthias W; Sutherland, Garnette R

    2017-01-01

    This study reports vibration profiles of neuronal cells and tissues as well as brain tumor and neocortical specimens. A contact-free method and analysis protocol was designed to convert an atomic force microscope into an ultra-sensitive microphone with capacity to record and listen to live biological samples. A frequency of 3.4 Hz was observed for both cultured rat hippocampal neurons and tissues and vibration could be modulated pharmacologically. Malignant astrocytoma tissue samples obtained from operating room, transported in artificial cerebrospinal fluid, and tested within an hour, vibrated with a much different frequency profile and amplitude, compared to meningioma or lateral temporal cortex providing a quantifiable measurement to accurately distinguish the three tissues in real-time. Vibration signals were converted to audible sound waves by frequency modulation, thus demonstrating, acoustic patterns unique to meningioma, malignant astrocytoma and neocortex.

  10. Brain tumor resection guided by fluorescence imaging

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  11. Vibrational Profiling of Brain Tumors and Cells

    PubMed Central

    Nelson, Sultan L; Proctor, Dustin T; Ghasemloonia, Ahmad; Lama, Sanju; Zareinia, Kourosh; Ahn, Younghee; Al-Saiedy, Mustafa R; Green, Francis HY; Amrein, Matthias W; Sutherland, Garnette R

    2017-01-01

    This study reports vibration profiles of neuronal cells and tissues as well as brain tumor and neocortical specimens. A contact-free method and analysis protocol was designed to convert an atomic force microscope into an ultra-sensitive microphone with capacity to record and listen to live biological samples. A frequency of 3.4 Hz was observed for both cultured rat hippocampal neurons and tissues and vibration could be modulated pharmacologically. Malignant astrocytoma tissue samples obtained from operating room, transported in artificial cerebrospinal fluid, and tested within an hour, vibrated with a much different frequency profile and amplitude, compared to meningioma or lateral temporal cortex providing a quantifiable measurement to accurately distinguish the three tissues in real-time. Vibration signals were converted to audible sound waves by frequency modulation, thus demonstrating, acoustic patterns unique to meningioma, malignant astrocytoma and neocortex. PMID:28744324

  12. Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth.

    PubMed

    Odland, Audun; Server, Andres; Saxhaug, Cathrine; Breivik, Birger; Groote, Rasmus; Vardal, Jonas; Larsson, Christopher; Bjørnerud, Atle

    2015-11-01

    Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed. To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined. Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics. A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample. Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs. © The Foundation Acta Radiologica 2014.

  13. 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/.

  14. 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.

  15. Brain Tumor Trials Collaborative | Center for Cancer Research

    Cancer.gov

    Brain Tumor Trials Collaborative In Pursuit of a Cure The mission of the BTTC is to develop and perform state-of-the-art clinical trials in a collaborative and collegial environment, advancing treatments for patients with brain tumors, merging good scientific method with concern for patient well-being and outcome.

  16. Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI.

    PubMed

    Nachimuthu, Deepa Subramaniam; Baladhandapani, Arunadevi

    2014-08-01

    This paper investigates the efficacy of automated pattern recognition methods on magnetic resonance data with the objective of assisting radiologists in the clinical diagnosis of brain tissue tumors. In this paper, the sciences of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are combined to improve the accuracy of the classifier, based on the multidimensional co-occurrence matrices to assess the detection of pathological tissues (tumor and edema), normal tissues (white matter - WM and gray matter - GM), and fluid (cerebrospinal fluid - CSF). The results show the ability of the classifier with iterative training to automatically and simultaneously recover tissue-specific spectral and structural patterns and achieve segmentation of tumor and edema and grading of high and low glioma tumor. Here, extreme learning machine - improved particle swarm optimization (ELM-IPSO) neural network classifier is trained with the feature descriptions in brain magnetic resonance (MR) spectra. This has the characteristics of varying the normal spectral pattern associated with tumor patterns along with imaging features. Validation was performed considering 35 clinical studies. The volumetric features extracted from the vectors of this matrix articulate some important elementary structures, which along with spectroscopic metabolite ratios discriminate the tumor grades and tissue classes. The quantitative 3D analysis reveals significant improvement in terms of global accuracy rate for automatic classification in brain tissues and discriminating pathological tumor tissue from structural healthy brain tissue.

  17. 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.

  18. 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.

  19. 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.

  20. Remodeling the blood-brain barrier microenvironment by natural products for brain tumor therapy.

    PubMed

    Zhao, Xiao; Chen, Rujing; Liu, Mei; Feng, Jianfang; Chen, Jun; Hu, Kaili

    2017-09-01

    Brain tumor incidence shows an upward trend in recent years; brain tumors account for 5% of adult tumors, while in children, this figure has increased to 70%. Moreover, 20%-30% of malignant tumors will eventually metastasize into the brain. Both benign and malignant tumors can cause an increase in intracranial pressure and brain tissue compression, leading to central nervous system (CNS) damage which endangers the patients' lives. Despite the many approaches to treating brain tumors and the progress that has been made, only modest gains in survival time of brain tumor patients have been achieved. At present, chemotherapy is the treatment of choice for many cancers, but the special structure of the blood-brain barrier (BBB) limits most chemotherapeutic agents from passing through the BBB and penetrating into tumors in the brain. The BBB microenvironment contains numerous cell types, including endothelial cells, astrocytes, peripheral cells and microglia, and extracellular matrix (ECM). Many chemical components of natural products are reported to regulate the BBB microenvironment near brain tumors and assist in their treatment. This review focuses on the composition and function of the BBB microenvironment under both physiological and pathological conditions, and the current research progress in regulating the BBB microenvironment by natural products to promote the treatment of brain tumors.

  1. Recruited brain tumor-derived mesenchymal stem cells contribute to brain tumor progression.

    PubMed

    Behnan, Jinan; Isakson, Pauline; Joel, Mrinal; Cilio, Corrado; Langmoen, Iver A; Vik-Mo, Einar O; Badn, Wiaam

    2014-05-01

    The identity of the cells that contribute to brain tumor structure and progression remains unclear. Mesenchymal stem cells (MSCs) have recently been isolated from normal mouse brain. Here, we report the infiltration of MSC-like cells into the GL261 murine glioma model. These brain tumor-derived mesenchymal stem cells (BT-MSCs) are defined with the phenotype (Lin-Sca-1+CD9+CD44+CD166+/-) and have multipotent differentiation capacity. We show that the infiltration of BT-MSCs correlates to tumor progression; furthermore, BT-MSCs increased the proliferation rate of GL261 cells in vitro. For the first time, we report that the majority of GL261 cells expressed mesenchymal phenotype under both adherent and sphere culture conditions in vitro and that the non-MSC population is nontumorigenic in vivo. Although the GL261 cell line expressed mesenchymal phenotype markers in vitro, most BT-MSCs are recruited cells from host origin in both wild-type GL261 inoculated into green fluorescent protein (GFP)-transgenic mice and GL261-GFP cells inoculated into wild-type mice. We show the expression of chemokine receptors CXCR4 and CXCR6 on different recruited cell populations. In vivo, the GL261 cells change marker profile and acquire a phenotype that is more similar to cells growing in sphere culture conditions. Finally, we identify a BT-MSC population in human glioblastoma that is CD44+CD9+CD166+ both in freshly isolated and culture-expanded cells. Our data indicate that cells with MSC-like phenotype infiltrate into the tumor stroma and play an important role in tumor cell growth in vitro and in vivo. Thus, we suggest that targeting BT-MSCs could be a possible strategy for treating glioblastoma patients. © 2013 AlphaMed Press.

  2. Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme.

    PubMed

    Fathi Kazerooni, Anahita; Mohseni, Meysam; Rezaei, Sahar; Bakhshandehpour, Gholamreza; Saligheh Rad, Hamidreza

    2015-02-01

    Glioblastoma multiforme (GBM) brain tumor is heterogeneous in nature, so its quantification depends on how to accurately segment different parts of the tumor, i.e. viable tumor, edema and necrosis. This procedure becomes more effective when metabolic and functional information, provided by physiological magnetic resonance (MR) imaging modalities, like diffusion-weighted-imaging (DWI) and perfusion-weighted-imaging (PWI), is incorporated with the anatomical magnetic resonance imaging (MRI). In this preliminary tumor quantification work, the idea is to characterize different regions of GBM tumors in an MRI-based semi-automatic multi-parametric approach to achieve more accurate characterization of pathogenic regions. For this purpose, three MR sequences, namely T2-weighted imaging (anatomical MR imaging), PWI and DWI of thirteen GBM patients, were acquired. To enhance the delineation of the boundaries of each pathogenic region (peri-tumoral edema, viable tumor and necrosis), the spatial fuzzy C-means algorithm is combined with the region growing method. The results show that exploiting the multi-parametric approach along with the proposed semi-automatic segmentation method can differentiate various tumorous regions with over 80 % sensitivity, specificity and dice score. The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the pre-surgical treatment planning.

  3. 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.

  4. 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.

  5. 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.

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

    PubMed

    Rossmeisl, John H

    2014-11-01

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

  7. 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

  8. CARS and non-linear microscopy imaging of brain tumors

    NASA Astrophysics Data System (ADS)

    Galli, Roberta; Uckermann, Ortrud; Tamosaityte, Sandra; Geiger, Kathrin; Schackert, Gabriele; Steiner, Gerald; Koch, Edmund; Kirsch, Matthias

    2013-06-01

    Nonlinear optical microscopy offers a series of techniques that have the potential to be applied in vivo, for intraoperative identification of tumor border and in situ pathology. By addressing the different content of lipids that characterize the tumors with respect to the normal brain tissue, CARS microscopy enables to discern primary and secondary brain tumors from healthy tissue. A study performed in mouse models shows that the reduction of the CARS signal is a reliable quantity to identify brain tumors, irrespective from the tumor type. Moreover it enables to identify tumor borders and infiltrations at a cellular resolution. Integration of CARS with autogenous TPEF and SHG adds morphological and compositional details about the tissue. Examples of multimodal CARS imaging of different human tumor biopsies demonstrate the ability of the technique to retrieve information useful for histopathological diagnosis.

  9. 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

  10. 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.

  11. Lassa-vesicular stomatitis chimeric virus safely destroys brain tumors.

    PubMed

    Wollmann, Guido; Drokhlyansky, Eugene; Davis, John N; Cepko, Connie; van den Pol, Anthony N

    2015-07-01

    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. 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 tested a series of

  12. 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

  13. What's New in Research and Treatment for Brain Tumors in Children?

    MedlinePlus

    ... Children What’s New in Research and Treatment for Brain and Spinal Cord Tumors in Children? There is ... and Spinal Cord Tumors in Children? More In Brain and Spinal Cord Tumors in Children About Brain ...

  14. Gamma Knife Surgery for Metastatic Brain Tumors from Gynecologic Cancer.

    PubMed

    Matsunaga, Shigeo; Shuto, Takashi; Sato, Mitsuru

    2016-05-01

    The incidences of metastatic brain tumors from gynecologic cancer have increased. The results of Gamma Knife surgery (GKS) for the treatment of patients with brain metastases from gynecologic cancer (ovarian, endometrial, and uterine cervical cancers) were retrospectively analyzed to identify the efficacy and prognostic factors for local tumor control and survival. The medical records were retrospectively reviewed of 70 patients with 306 tumors who underwent GKS for brain metastases from gynecologic cancer between January 1995 and December 2013 in our institution. The primary cancers were ovarian in 33 patients with 147 tumors and uterine in 37 patients with 159 tumors. Median tumor volume was 0.3 cm(3). Median marginal prescription dose was 20 Gy. The local tumor control rates were 96.4% at 6 months and 89.9% at 1 year. There was no statistically significant difference between ovarian and uterine cancers. Higher prescription dose and smaller tumor volume were significantly correlated with local tumor control. Median overall survival time was 8 months. Primary ovarian cancer, controlled extracranial metastases, and solitary brain metastasis were significantly correlated with satisfactory overall survival. Median activities of daily living (ADL) preservation survival time was 8 months. Primary ovarian cancer, controlled extracranial metastases, and higher Karnofsky Performance Status score were significantly correlated with better ADL preservation. GKS is effective for control of tumor progression in patients with brain metastases from gynecologic cancer, and may provide neurologic benefits and preservation of the quality of life. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. 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.

  16. Brain anatomical structure segmentation by hybrid discriminative/generative models.

    PubMed

    Tu, Z; Narr, K L; Dollar, P; Dinov, I; Thompson, P M; Toga, A W

    2008-04-01

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

  17. 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.

  18. 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.

  19. Identification of candidate cancer-causing genes in mouse brain tumors by retroviral tagging

    PubMed Central

    Johansson, Fredrik K.; Brodd, Josefin; Eklöf, Charlotta; Ferletta, Maria; Hesselager, Göran; Tiger, Carl-Fredrik; Uhrbom, Lene; Westermark, Bengt

    2004-01-01

    Murine retroviruses may cause malignant tumors in mice by insertional mutagenesis of host genes. The use of retroviral tagging as a means of identifying cancer-causing genes has, however, almost entirely been restricted to hematopoietic tumors. The aim of this study was to develop a system allowing for the retroviral tagging of candidate genes in malignant brain tumors. Mouse gliomas were induced by a recombinant Moloney murine leukemia virus encoding platelet-derived growth factor (PDGF) B-chain. The underlying idea was that tumors evolve through a combination of PDGF-mediated autocrine growth stimulation and insertional mutagenesis of genes that cooperate with PDGF in gliomagenesis. Common insertion sites (loci that were tagged in more than one tumor) were identified by cloning and sequencing retroviral flanking segments, followed by blast searches of mouse genome databases. A number of candidate brain tumor loci (Btls) were identified. Several of these Btls correspond to known tumor-causing genes; these findings strongly support the underlying idea of our experimental approach. Other Btls harbor genes with a hitherto unproven role in transformation or oncogenesis. Our findings indicate that retroviral tagging with a growth factor-encoding virus may be a powerful means of identifying candidate tumor-causing genes in nonhematopoietic tumors. PMID:15273287

  20. Hypofractionation Regimens for Stereotactic Radiotherapy for Large Brain Tumors

    SciTech Connect

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

    2008-10-01

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

  1. Hypofractionation regimens for stereotactic radiotherapy for large brain tumors.

    PubMed

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

    2008-10-01

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

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

    PubMed

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

    2016-04-01

    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.

  3. 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.

  4. NGAL immunohistochemical expression in brain primary and metastatic tumors.

    PubMed

    Barresi, V; Tuccari, G; Barresi, G

    2010-01-01

    A significant association has been recently shown between the expression of neutrophil gelatinase-associated lipocalin (NGAL) in tumors and its urinary levels. Thus NGAL urinary detection has been proposed as a method for the early diagnosis of brain tumors. In view of this, the objective of this study was to investigate whether NGAL expression differs according to brain tumor type or in primary vs. metastatic brain neolasias. 42 surgically resected formalin fixed and paraffin embedded neoplasias, including 15 cases of brain metastasis and 27 cases of primary central nervous system (CNS) tumors (11 meningiomas; 1 pilocytic astrocytoma, 2 diffuse astrocytomas, 2 oligoastrocytomas, 2 oligodendrogliomas, 1 anaplastic oligoastrocytoma, 7 glioblastomas, 1 ependymoma) were submitted to the immunohistochemical procedure. Sections were incubated overnight with the primary antibody against NGAL. NGAL staining was found in all the analyzed glioblastomas and in the anaplastic oligoastrocytoma. No NGAL immuno-expression was evidenced in all the other cases. A statistically significant correlation was demonstrated between NGAL presence and high proliferation index in the primary tumors. In conclusion, our findings suggest that NGAL expression is restricted to high grade gliomas among primary brain tumors, and that brain metastases do not express this protein. Considering the correlation between NGAL expression in tumors and its urinary levels, if our observations will be further validated, NGAL urinary detection might be used as an additional tool in the pre-surgical definition of brain lesions involving difficult differential diagnosis.

  5. 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

  6. 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

  7. Novel treatment strategies for brain tumors and metastases.

    PubMed

    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

    2014-05-01

    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.

  8. Absence of pathogenic mitochondrial DNA mutations in mouse brain tumors

    PubMed Central

    Kiebish, Michael A; Seyfried, Thomas N

    2005-01-01

    Background Somatic mutations in the mitochondrial genome occur in numerous tumor types including brain tumors. These mutations are generally found in the hypervariable regions I and II of the displacement loop and unlikely alter mitochondrial function. Two hypervariable regions of mononucleotide repeats occur in the mouse mitochondrial genome, i.e., the origin of replication of the light strand (OL) and the Arg tRNA. Methods In this study we examined the entire mitochondrial genome in a series of chemically induced brain tumors in the C57BL/6J strain and spontaneous brain tumors in the VM mouse strain. The tumor mtDNA was compared to that of mtDNA in brain mitochondrial populations from the corresponding syngeneic mouse host strain. Results Direct sequencing revealed a few homoplasmic base pair insertions, deletions, and substitutions in the tumor cells mainly in regions of mononucleotide repeats. A heteroplasmic mutation in the 16srRNA gene was detected in a spontaneous metastatic VM brain tumor. Conclusion None of the mutations were considered pathogenic, indicating that mtDNA somatic mutations do not likely contribute to the initiation or progression of these diverse mouse brain tumors. PMID:16105171

  9. Automatic corpus callosum segmentation for standardized MR brain scanning

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

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

  10. 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

  11. [Functional imaging for brain tumors (perfusion, DTI and MR spectroscopy)].

    PubMed

    Essig, M; Giesel, F; Stieltjes, B; Weber, M A

    2007-06-01

    This contribution considers the possibilities involved with using functional methods in magnetic resonance imaging (MRI) diagnostics for brain tumors. Of the functional methods available, we discuss perfusion MRI (PWI), diffusion MRI (DWI and DTI) and MR spectroscopy (H-MRS). In cases of brain tumor, PWI aids in grading and better differentiation in diagnostics as well as for pre-therapeutic planning. In addition, the course of treatment, both after chemo- as well as radiotherapy in combination with surgical treatment, can be optimized. PWI allows better estimates of biological activity and aggressiveness in low grade brain tumors, and in the case of WHO grade II astrocytoma showing anaplasically transformed tumor areas, allows more rapid visu-alization and a better prediction of the course of the disease than conventional MRI diagnostics. Diffusion MRI, due to the directional dependence of the diffusion, can illustrate the course and direction of the nerve fibers, as well as reconstructing the nerve tracts in the cerebrum, pons and cerebellum 3-dimensionally. Diffusion imaging can be used for describing brain tumors, for evaluating contralateral involvement and the course of the nerve fibers near the tumor. Due to its operator dependence, DTI based fiber tracking for defining risk structures is controversial. DWI can also not differentiate accurately between cystic and necrotic brain tumors, or between metastases and brain abscesses. H-MRS provides information on cell membrane metabolism, neuronal integrity and the function of neuronal structures, energy metabolism and the formation of tumors and brain tissue necroses. Diagnostic problems such as the differentiation between neoplastic and non-neoplastic lesions, grading cerebral glioma and distinguishing between primary brain tumors and metastases can be resolved. An additional contribution will discuss the control of the course of glial tumors after radiotherapy.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. Spectral and lifetime domain measurements of rat brain tumors.

    PubMed

    Haidar, D Abi; Leh, B; Zanello, M; Siebert, R

    2015-04-01

    During glioblastoma surgery, delineation of the brain tumor margins is difficult because the infiltrated and normal tissues have the same visual appearance. We use a fiber-optical fluorescence probe for spectroscopic and time domain measurements to assist surgeon in differentiating the healthy and the infiltrated tissues. First study was performed on rats that were previously injected with tumorous cells. Measurements of endogenous tissue fluorescence were performed on fresh and fixed rat tumor brain slices. Spectral characteristics, fluorescence redox ratios and fluorescence lifetime measurements were analyzed. The study aimed at defining an optical index that can act as an indicator for discriminating healthy from tumorous tissue.

  18. 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.

  19. 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.

  20. Nonconvulsive status epilepticus in patients with brain tumors.

    PubMed

    Marcuse, Lara V; Lancman, Guido; Demopoulos, Alexis; Fields, Madeline

    2014-08-01

    The prevalence of nonconvulsive status epilepticus (NCSE) in brain tumor patients is unknown. Since NCSE has been associated with significant mortality and morbidity, early identification is essential. This study describes the clinical and EEG characteristics, treatment, and outcome in brain tumor patients with NCSE. All patients admitted to Mount Sinai Hospital from 2009 to 2012 with an ICD-9 brain tumor code were cross-referenced with the epilepsy department's database. EEGs from matching patients were reviewed for NCSE. Relevant information from the medical records of the patients with NCSE was extracted. 1101 brain tumor patients were identified, of which 259 (24%) had an EEG and 24 (2%) had NCSE. The vast majority of seizures captured were subclinical with 13 patients (54%) having only subclinical seizures. Treatment resolved the NCSE in 22 patients (92%) with accompanying clinical improvement in 18 (75%) of those patients. Tumor recurrence or progression on MRI was associated with decreased 2-month survival (75% mortality, p=0.035) compared to stable tumors (20% mortality). Patients with metastatic disease had median survival from tumor diagnosis of 1.2 months. NCSE in brain tumor patients may be under diagnosed due to the frequent lack of outward manifestations and highly treatable with improvement in the majority of patients. NCSE patients with progressing brain lesions, tumor recurrence, or metastatic disease are at serious risk of mortality within 2 months. Continuous EEG monitoring in brain tumor patients with recent clinical seizures and/or a depressed level of consciousness may be critical in providing appropriate care. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  1. 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.

  2. Culture and isolation of brain tumor initiating cells.

    PubMed

    Lenkiewicz, Monika; Li, Na; Singh, Sheila K

    2009-10-01

    This unit describes protocols for the culture and isolation of brain tumor initiating cells (BTIC). The cancer stem cell (CSC) hypothesis suggests that tumors are maintained exclusively by a rare fraction of cells that have stem cell properties. We applied culture conditions and assays originally used for normal neural stem cells (NSCs) in vitro to a variety of brain tumors. The BTIC were isolated by fluorescence activated cell sorting for the neural precursor cell surface marker CD133. Only the CD133(+) brain tumor fraction contains cells capable of sphere formation and sustained self-renewal in vitro, and tumor initiation in NOD-SCID mouse brains. Therefore, CD133(+) BTICs satisfy the definition of cancer stem cells in that they are able to generate a replica of the patient's tumor and they exhibit self-renewal ability through serial retransplantation. This established that only a rare subset of brain tumor cells with stem cell properties are tumor-initiating, and, in this unit, we describe their culture and isolation.

  3. 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.

  4. 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.

  5. Pattern recognition of MRSI data shows regions of glioma growth that agree with DTI markers of brain tumor infiltration.

    PubMed

    Wright, Alan J; Fellows, G; Byrnes, T J; Opstad, K S; McIntyre, D J O; Griffiths, J R; Bell, B A; Clark, C A; Barrick, T R; Howe, F A

    2009-12-01

    Gliomas are the most common primary brain tumors and the majority are highly malignant, with one of the worst prognoses for patients. Gliomas are characterized by invasive growth into normal brain tissue that makes complete surgical resection and accurate radiotherapy planning extremely difficult. We have performed independent component analysis of magnetic resonance spectroscopy imaging data from human gliomas to segment brain tissue into tumor core, tumor infiltration, and normal brain, with confirmation by diffusion tensor imaging analysis. Our data are consistent with previous studies that compared anomalies in isotropic and anisotropic diffusion images to determine regions of potential glioma infiltration. We show that coefficients of independent components can be used to create colored images for easy visual identification of regions of infiltrative tumor growth. (c) 2009 Wiley-Liss, Inc.

  6. 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.

  7. Local specific absorption rate in brain tumors at 7 tesla.

    PubMed

    Restivo, Matthew C; van den Berg, Cornelis A T; van Lier, Astrid L H M W; Polders, Daniël L; Raaijmakers, Alexander J E; Luijten, Peter R; Hoogduin, Hans

    2016-01-01

    MR safety at 7 Tesla relies on accurate numerical simulations of transmit electromagnetic fields to fully assess local specific absorption rate (SAR) safety. Numerical simulations for SAR safety are currently performed using models of healthy patients. These simulations might not be useful for estimating SAR in patients who have large lesions with potentially abnormal dielectric properties, e.g., brain tumors. In this study, brain tumor patient models are constructed based on scans of four patients with high grade brain tumors. Dielectric properties for the modeled tumors are assigned based on electrical properties tomography data for the same patients. Simulations were performed to determine SAR. Local SAR increases in the tumors by as much as 30%. However, the location of the maximum 10-gram averaged SAR typically occurs outside of the tumor, and thus does not increase. In the worst case, if the tumor model is moved to the location of maximum electric field intensity, then we do observe an increase in the estimated peak 10-gram SAR directly related to the tumor. Peak local SAR estimation made on the results of a healthy patient model simulation may underestimate the true peak local SAR in a brain tumor patient. © 2015 Wiley Periodicals, Inc.

  8. Examination of Blood-Brain Barrier (BBB) Integrity In A Mouse Brain Tumor Model

    PubMed Central

    On, Ngoc; Mitchell, Ryan; Savant, Sanjot D.; Bachmeier, Corbin. J.; Hatch, Grant M.; Miller, Donald W.

    2013-01-01

    The present study evaluates, both functionally and biochemically, brain tumor-induced alterations in brain capillary endothelial cells. Brain tumors were induced in Balb/c mice via intracranial injection of Lewis Lung carcinoma (3LL) cells into the right hemisphere of the mouse brain using stereotaxic apparatus. Blood-brain barrier (BBB) permeability was assessed at various stages of tumor development, using both radiolabeled tracer permeability and magnetic resonance imaging (MRI) with gadolinium diethylene-triamine-pentaacetate contrast enhancement (Gad-DTPA). The expression of the drug efflux transporter, P-glycoprotein (P-gp), in the BBB at various stages of tumor development was also evaluated by Western blot and immunohistochemistry. Median mouse survival following tumor cell injection was 17 days. The permeability of the BBB to 3H-mannitol was similar in both brain hemispheres at 7 and 10 days post-injection. By day 15, there was a 2-fold increase in 3H-mannitol permeability in the tumor bearing hemispheres compared to the non-tumor hemispheres. Examination of BBB permeability with Gad-DTPA contrast enhanced MRI indicated cerebral vascular permeability changes were confined to the tumor area. The permeability increase observed at the later stages of tumor development correlated with an increase in cerebral vascular volume suggesting angiogenesis within the tumor bearing hemisphere. Furthermore, the Gad-DPTA enhancement observed within the tumor area was significantly less than Gad-DPTA enhancement within the circumventricular organs not protected by the BBB. Expression of P-gp in both the tumor bearing and non-tumor bearing portions of the brain appeared similar at all time points examined. These studies suggest that although BBB integrity is altered within the tumor site at later stages of development, the BBB is still functional and limiting in terms of solute and drug permeability in and around the tumor. PMID:23184143

  9. 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.

  10. 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 ...

  11. 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.

  12. Modeling and Targeting MYC Genes in Childhood Brain Tumors.

    PubMed

    Hutter, Sonja; Bolin, Sara; Weishaupt, Holger; Swartling, Fredrik J

    2017-03-23

    Brain tumors are the second most common group of childhood cancers, accounting for about 20%-25% of all pediatric tumors. Deregulated expression of the MYC family of transcription factors, particularly c-MYC and MYCN genes, has been found in many of these neoplasms, and their expression levels are often correlated with poor prognosis. Elevated c-MYC/MYCN initiates and drives tumorigenesis in many in vivo model systems of pediatric brain tumors. Therefore, inhibition of their oncogenic function is an attractive therapeutic target. In this review, we explore the roles of MYC oncoproteins and their molecular targets during the formation, maintenance, and recurrence of childhood brain tumors. We also briefly summarize recent progress in the development of therapeutic approaches for pharmacological inhibition of MYC activity in these tumors.

  13. Modeling and Targeting MYC Genes in Childhood Brain Tumors

    PubMed Central

    Hutter, Sonja; Bolin, Sara; Weishaupt, Holger; Swartling, Fredrik J.

    2017-01-01

    Brain tumors are the second most common group of childhood cancers, accounting for about 20%–25% of all pediatric tumors. Deregulated expression of the MYC family of transcription factors, particularly c-MYC and MYCN genes, has been found in many of these neoplasms, and their expression levels are often correlated with poor prognosis. Elevated c-MYC/MYCN initiates and drives tumorigenesis in many in vivo model systems of pediatric brain tumors. Therefore, inhibition of their oncogenic function is an attractive therapeutic target. In this review, we explore the roles of MYC oncoproteins and their molecular targets during the formation, maintenance, and recurrence of childhood brain tumors. We also briefly summarize recent progress in the development of therapeutic approaches for pharmacological inhibition of MYC activity in these tumors. PMID:28333115

  14. 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

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

    PubMed

    Yacoob, Sulafa M; Hassan, Noha S

    2012-08-14

    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. 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. 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. 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.

  16. 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

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

    PubMed

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

    2014-03-01

    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. 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 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. 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.

  18. 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

  19. 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.

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

    PubMed Central

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

    2016-01-01

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

  1. 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).

  2. 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.

  3. 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

  4. Brain tumors in children--current therapies and newer directions.

    PubMed

    Khatua, Soumen; Sadighi, Zsila Sousan; Pearlman, Michael L; Bochare, Sunil; Vats, Tribhawan S

    2012-07-01

    Brain tumors are the second most common malignancy and the major cause of cancer related mortality in children. Though significant advances in neuroimaging, neurosurgery, radiation therapy and chemotherapy have evolved over the years, overall survival rate remains less than 75%. Malignant gliomas, high risk medulloblastoma with recurrence and infant brain tumors continue to be a major cause of therapeutic frustration. Even today diffuse pontine gliomas are universally fatal. Though tumors like low grade glioma have an overall excellent survival, recurrences and progression in eloquent areas pose therapeutic challenges. As research continues to unravel the biology including key molecules and signaling pathways responsible for the oncogenesis of different childhood brain tumors, novel targeted therapies are profiled. Identification of major targets like the Epidermal Growth factor Receptor (EGFR), Platelet Derived Growth Factor Receptor (PDGFR), Vascular Endothelial Growth factor (VEGF) and key signaling pathways like the MAPK and PI3K/Akt/mTOR has enabled us over the recent years to better understand tumor behavior and design tailored therapy. These efforts have improved overall survival of children with brain tumors. This review article discusses the current status of common brain tumors in children and the newer therapeutic approaches.

  5. 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

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

    NASA Astrophysics Data System (ADS)

    Wang, Zhihui; Deisboeck, Thomas S.

    2008-04-01

    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 silicobrain 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  13. Molecular imaging of brain tumors with radiolabeled choline PET.

    PubMed

    Calabria, Ferdinando Franco; Barbarisi, Manlio; Gangemi, Vincenzo; Grillea, Giovanni; Cascini, Giuseppe Lucio

    2016-05-26

    Several positron emission tomography (PET) radiopharmaceuticals have been emerged in the last decade as feasible in the management of brain lesions, due to the low performance in this field of the 18F-fluoro-deoxyglucose (18F-FDG), for its high physiological gradient of distribution in the brain. Beyond its usefulness in prostate cancer imaging, the radiolabeled choline is becoming a promising tool in diagnosing benign and malignant lesions of the brain, due to a very low rate of distribution in normal white and grey matters. The aim of our review was to assess the real impact of the radiolabeled choline PET/CT in the management of brain benign lesions, brain tumors, and metastases. Furthermore, emphasis was given to the comparison between the radiolabeled choline and the other radiopharmaceuticals in this field. A literature review was performed. The radiolabeled choline is useful in the management of patients with suspected brain tumor relapse, especially in association with magnetic resonance imaging (MRI), with caution regarding its intrinsic characteristic of non-tumor-specific tracer. For the same reason, it is not useful in the early evaluation of brain lesions. Similar results are reported for other radiopharmaceuticals. The inclusion of the head in the whole-body scans for somatic tumors is necessary to ensure metastases in the brain or choline-avid benign lesions.

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

    NASA Astrophysics Data System (ADS)

    Li, Kang; Jolly, Marie-Pierre

    2008-03-01

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

  15. 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.

  16. 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

  17. 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.

  18. Identification of a cancer stem cell in human brain tumors.

    PubMed

    Singh, Sheila K; Clarke, Ian D; Terasaki, Mizuhiko; Bonn, Victoria E; Hawkins, Cynthia; Squire, Jeremy; Dirks, Peter B

    2003-09-15

    Most current research on human brain tumors is focused on the molecular and cellular analysis of the bulk tumor mass. However, there is overwhelming evidence in some malignancies that the tumor clone is heterogeneous with respect to proliferation and differentiation. In human leukemia, the tumor clone is organized as a hierarchy that originates from rare leukemic stem cells that possess extensive proliferative and self-renewal potential, and are responsible for maintaining the tumor clone. We report here the identification and purification of a cancer stem cell from human brain tumors of different phenotypes that possesses a marked capacity for proliferation, self-renewal, and differentiation. The increased self-renewal capacity of the brain tumor stem cell (BTSC) was highest from the most aggressive clinical samples of medulloblastoma compared with low-grade gliomas. The BTSC was exclusively isolated with the cell fraction expressing the neural stem cell surface marker CD133. These CD133+ cells could differentiate in culture into tumor cells that phenotypically resembled the tumor from the patient. The identification of a BTSC provides a powerful tool to investigate the tumorigenic process in the central nervous system and to develop therapies targeted to the BTSC.

  19. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

    PubMed

    Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

    2017-08-01

    Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

  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. 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

  2. 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.

  3. 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

  4. 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.

  5. [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.

  6. Radiation therapy for older patients with brain tumors.

    PubMed

    Minniti, Giuseppe; Filippi, Andrea Riccardo; Osti, Mattia Falchetto; Ricardi, Umberto

    2017-06-19

    The incidence of brain tumors in the elderly population has increased over the last few decades. Current treatment includes surgery, radiotherapy and chemotherapy, but the optimal management of older patients with brain tumors remains a matter of debate, since aggressive radiation treatments in this population may be associated with high risks of neurological toxicity and deterioration of quality of life. For such patients, a careful clinical status assessment is mandatory both for clinical decision making and for designing randomized trials to adequately evaluate the optimal combination of radiotherapy and chemotherapy.Several randomized studies have demonstrated the efficacy and safety of chemotherapy for patients with glioblastoma or lymphoma; however, the use of radiotherapy given in association with chemotherapy or as salvage therapy remains an effective treatment option associated with survival benefit. Stereotactic techniques are increasingly used for the treatment of patients with brain metastases and benign tumors, including pituitary adenomas, meningiomas and acoustic neuromas. Although no randomized trials have proven the superiority of SRS over other radiation techniques in older patients with brain metastases or benign brain tumors, data extracted from recent randomized studies and large retrospective series suggest that SRS is an effective approach in such patients associated with survival advantages and toxicity profile similar to those observed in young adults. Future trials need to investigate the optimal radiation techniques and dose/fractionation schedules in older patients with brain tumors with regard to clinical outcomes, neurocognitive function, and quality of life.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. CT of irradiated solid tumor metastases to the brain.

    PubMed

    Brown, S B; Brant-Zawadzki, M; Eifel, P; Coleman, C N; Enzmann, D R

    1982-01-01

    Twenty patients with solid tumor metastases to the brain, demonstrated by CT scanning, had follow-up scans after radiation therapy of the metastatic focus. Nine patients (45%) showed no evidence of the metastasis on the initial follow-up scans. Another 10 patients (50%) showed some improvement in the size, enhancement, or surrounding edema of the lesion. Only one patient showed progression in spite of therapy. The CT scan identified those patients who achieved longer survival and/or longer time intervals before brain relapse. However, CT scans must be interpreted with caution in patients still on corticosteroid treatment. Additionally, other non-tumoral conditions may mimic tumor recurrence. Radiation therapy offered palliation in patients with brain metastases, and in some instances, sterilized patients of their metastatic brain involvement.

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

    PubMed

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

    2015-10-01

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

  14. SU-C-207B-03: A Geometrical Constrained Chan-Vese Based Tumor Segmentation Scheme for PET

    SciTech Connect

    Chen, L; Zhou, Z; Wang, J

    2016-06-15

    Purpose: Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan-Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices. Methods: The proposed scheme performs segmentation in a slice-by-slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering-based segmentation method under user’s guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan-Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan-Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme. Results: Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively. Conclusion: We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

  15. The social trajectory of brain tumor: a qualitative metasynthesis.

    PubMed

    Cubis, Lee; Ownsworth, Tamara; Pinkham, Mark B; Chambers, Suzanne

    2017-04-19

    Research indicates that strong social ties can buffer the adverse effects of chronic illness on psychological well-being. Brain tumor typically leads to serious functional impairments that affect relationships and reduce social participation. This metasynthesis aimed to identify, appraise and integrate the findings of qualitative studies that reveal the impact of brain tumor on social networks. Four major databases (PubMed, CINAHL, Cochrane Library and PsycINFO) were systematically searched from inception to September 2016 for qualitative studies that reported findings on the impact of primary brain tumor on social networks during adulthood. Twenty-one eligible studies were identified and appraised according to the Consolidated Criteria for Reporting Qualitative Research. Key findings of these studies were integrated to form superordinate themes. The metasynthesis revealed the core themes of: 1) Life disrupted; 2) Navigating the new reality of life; and 3) Social survivorship versus separation. Multiple changes typically occur across the social trajectory of brain tumor, including a loss of pre-illness networks and the emergence of new ones. Understanding the barriers and facilitators for maintaining social connection may guide interventions for strengthening social networks and enhancing well-being in the context of brain tumor. Implications for rehabilitation Social networks and roles are disrupted throughout the entire trajectory of living with brain tumor Physical, cognitive and psychological factors represent barriers to social integration Barriers to social integration may be addressed by supportive care interventions Compensatory strategies, adjusting goals and expectations, educating friends and family and accepting support from others facilitate social reintegration throughout the trajectory of living with brain tumor.

  16. Orthotopic models of pediatric brain tumors in zebrafish

    PubMed Central

    Eden, Christopher J.; Ju, Bensheng; Murugesan, Mohankumar; Phoenix, Timothy; Nimmervoll, Birgit; Tong, Yiai; Ellison, David W.; Finkelstein, David; Wright, Karen; Boulos, Nidal; Dapper, Jason; Thiruvenkatam, Radhika; Lessman, Charles; Taylor, Michael R.; Gilbertson, Richard J.

    2014-01-01

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

  17. Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404

    NASA Astrophysics Data System (ADS)

    Besemer, Abigail E.; Titz, Benjamin; Grudzinski, Joseph J.; Weichert, Jamey P.; Kuo, John S.; Robins, H. Ian; Hall, Lance T.; Bednarz, Bryan P.

    2017-08-01

    Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131I-CLR1404 voxel-level dose distribution was calculated from the 124I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average  ±  standard deviation (range) was 0.19  ±  0.13 (0.01-0.51), 0.30  ±  0.17 (0.03-0.67), and 0.75  ±  0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq-1 (0.07-0.37 Gy GBq-1). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard

  18. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed

    Park, Keun Young; Kim, Byung Moon; Kim, Dong Joon

    2016-01-01

    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. 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. 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. Preoperative coiling and subsequent tumor surgery was safe and effective, making it a reasonable treatment option for patients with brain tumor and cUIA.

  1. 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

  2. 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.

  3. 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

  4. Biodegradable brain-penetrating DNA nanocomplexes and their use to treat malignant brain tumors.

    PubMed

    Mastorakos, Panagiotis; Zhang, Clark; Song, Eric; Kim, Young Eun; Park, Hee Won; Berry, Sneha; Choi, Won Kyu; Hanes, Justin; Suk, Jung Soo

    2017-09-28

    The discovery of powerful genetic targets has spurred clinical development of gene therapy approaches to treat patients with malignant brain tumors. However, lack of success in the clinic has been attributed to the inability of conventional gene vectors to achieve gene transfer throughout highly disseminated primary brain tumors. Here, we demonstrate ex vivo that small nanocomplexes composed of DNA condensed by a blend of biodegradable polymer, poly(β-amino ester) (PBAE), with PBAE conjugated with 5kDa polyethylene glycol (PEG) molecules (PBAE-PEG) rapidly penetrate healthy brain parenchyma and orthotopic brain tumor tissues in rats. Rapid diffusion of these DNA-loaded nanocomplexes observed in fresh tissues ex vivo demonstrated that they avoided adhesive trapping in the brain owing to their dense PEG coating, which was critical to achieving widespread transgene expression throughout orthotopic rat brain tumors in vivo following administration by convection enhanced delivery. Transgene expression with the PBAE/PBAE-PEG blended nanocomplexes (DNA-loaded brain-penetrating nanocomplexes, or DNA-BPN) was uniform throughout the tumor core compared to nanocomplexes composed of DNA with PBAE only (DNA-loaded conventional nanocomplexes, or DNA-CN), and transgene expression reached beyond the tumor edge, where infiltrative cancer cells are found, only for the DNA-BPN formulation. Finally, DNA-BPN loaded with anti-cancer plasmid DNA provided significantly enhanced survival compared to the same plasmid DNA loaded in DNA-CN in two aggressive orthotopic brain tumor models in rats. These findings underscore the importance of achieving widespread delivery of therapeutic nucleic acids within brain tumors and provide a promising new delivery platform for localized gene therapy in the brain. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

  7. 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.

  8. Brain and Spinal Tumors: Hope through Research

    MedlinePlus

    ... traits of CNS tumors. [1] Carcinogenicity of radiofrequency electromagnetic fields. World Health Organization/International Agency for Research ... Information Page Todd's Paralysis Information Page NINDS Autism Spectrum Disorder ... Page Transmissible Spongiform Encephalopathies Information Page ...

  9. An automated image segmentation and classification algorithm for immunohistochemically stained tumor cell nuclei

    NASA Astrophysics Data System (ADS)

    Yeo, Hangu; Sheinin, Vadim; Sheinin, Yuri

    2009-02-01

    As medical image data sets are digitized and the number of data sets is increasing exponentially, there is a need for automated image processing and analysis technique. Most medical imaging methods require human visual inspection and manual measurement which are labor intensive and often produce inconsistent results. In this paper, we propose an automated image segmentation and classification method that identifies tumor cell nuclei in medical images and classifies these nuclei into two categories, stained and unstained tumor cell nuclei. The proposed method segments and labels individual tumor cell nuclei, separates nuclei clusters, and produces stained and unstained tumor cell nuclei counts. The representative fields of view have been chosen by a pathologist from a known diagnosis (clear cell renal cell carcinoma), and the automated results are compared with the hand-counted results by a pathologist.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. Groupwise registration of MR brain images with tumors

    NASA Astrophysics Data System (ADS)

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-09-01

    A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of ‘image registration paths’ to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10-9).

  15. Groupwise registration of MR brain images with tumors.

    PubMed

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-08-04

    A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of 'image registration paths' to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10(-9)).

  16. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-02-01

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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Contour-based brain segmentation method for magnetic resonance imaging human head scans.

    PubMed

    Somasundaram, K; Kalavathi, P

    2013-01-01

    The high-resolution magnetic resonance brain images often contain some nonbrain tissues (ie, skin, fat, muscle, neck, eye balls, etc) compared with the functional images such as positron emission tomography, single-photon emission computed tomography, and functional magnetic resonance imaging (MRI) scans, which usually contain few nonbrain tissues. Automatic segmentation of brain tissues from MRI scans remains a challenging task due to the variation in shape and size, use of different pulse sequences, overlapping signal intensities and imaging artifacts. This article presents a contour-based automatic brain segmentation method to segment the brain regions from T1-, T2-, and proton density-weighted MRI of human head scans. The proposed method consists of 2 stages. In stage 1, the brain regions in the middle slice is extracted. Many of the existing methods failed to extract brain regions in the lower and upper slices of the brain volume, where the brain appears in more than 1 connected region. To overcome this problem, in the proposed method, a landmark circle is drawn at the center of the extracted brain region of a middle slice and is likely to pass through all the brain regions in the remaining lower and upper slices irrespective of whether the brain is composed of 1 or more connected components. In stage 2, the brain regions in the remaining slices are extracted with reference to the landmark circle obtained in stage 1. The proposed method is robust to the variability of brain anatomy, image orientation, and image type, and it extracts the brain regions accurately in T1-, T2-, and proton density-weighted normal and abnormal brain images. Experimental results by applying the proposed method on 100 volumes of brain images show that the proposed method exhibits best and consistent performance than by the popular existing methods brain extraction tool, brain surface extraction, watershed algorithm, hybrid watershed algorithm, and skull stripping using graph cuts.

  3. Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations

    PubMed Central

    Li, Laquan; Wang, Jian; Lu, Wei; Tan, Shan

    2016-01-01

    Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L2 regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L2 regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ-convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin’s lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the

  4. Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations.

    PubMed

    Li, Laquan; Wang, Jian; Lu, Wei; Tan, Shan

    2017-02-01

    Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L2 regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L2 regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ-convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin's lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the axial

  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. 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. Copyright © 2015 John Wiley & Sons, Inc.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Establishment of a tumor sphere cell line from a metastatic brain neuroendocrine tumor.

    PubMed

    Iwata, Ryoichi; Maruyama, Masato; Ito, Tomoki; Nakano, Yosuke; Kanemura, Yonehiro; Koike, Taro; Oe, Souichi; Yoshimura, Kunikazu; Nonaka, Masahiro; Nomura, Shosaku; Sugimoto, Tetsuo; Yamada, Hisao; Asai, Akio

    2017-05-17

    Neuroendocrine tumors are rare, and little is known about the existence of cancer stem cells in this disease. Identification of the tumorigenic population will contribute to the development of effective therapies targeting neuroendocrine tumors. Surgically resected brain metastases from a primary neuroendocrine tumor of unknown origin were dissociated and cultured in serum-free neurosphere medium. Stem cell properties, including self-renewal, differentiation potential, and stem cell marker expression, were examined. Tumor formation was evaluated using intracranial xenograft models. The effect of temozolomide was measured in vitro by cell viability assays. We established the neuroendocrine tumor sphere cell line ANI-27S, which displayed stable exponential growth, virtually unlimited expansion in vitro, and expression of stem-cell markers such as CD133, nestin, Sox2, and aldehyde dehydrogenase. FBS-induced differentiation decreased Sox2 and nestin expression. On the basis of real-time PCR, ANI-27S cells expressed the neuroendocrine markers synaptophysin and chromogranin A. Intracranial xenotransplanted brain tumors recapitulated the original patient tumor and temozolomide exhibited cytotoxic effects on tumor sphere cells. For the first time, we demonstrated the presence of a sphere-forming, stem cell-like population in brain metastases from a primary neuroendocrine tumor. We also demonstrated the potential therapeutic effects of temozolomide for this disease.

  13. Hierarchical approach for automated segmentation of the brain volume from MR images

    NASA Astrophysics Data System (ADS)

    Hsu, Li-Yueh; Loew, Murray H.; Momenan, Reza

    1999-05-01

    Image segmentation is considered one of the essential steps in medical image analysis. Cases such as classification of tissue structures for quantitative analysis, reconstruction of anatomical volumes for visualization, and registration of multi-modality images for complementary study often require the segmentation of the brain to accomplish the task. In many clinical applications, parts of this task are performed either manually or interactively. Not only is this proces often tedious and time-consuming, it introduces additional external factors of inter- and intra-rater variability. In this paper, we present a 3D automated algorithm for segmenting the brain from various MR images. This algorithm consists of a sequence of pre-determined steps: First, an intensity window for initial separation of the brain volume from the background and non-brain structures is selected by using probability curves fitting on the intensity histogram. Next, a 3D isotropic volume is interpolated and an optimal threshold value is determined to construct a binary brain mask. The morphological and connectivity processes are then applied on this 3D mask for eliminating the non-brain structures. Finally, a surface extraction kernel is applied to extract the 3D brain surface. Preliminary results from the same subjects with different pulse sequences are compared with the manual segmentation. The automatically segmented brain volumes are compared with the manual results using the correlation coefficient and percentage overlay. Then the automatically detected surfaces are measured with the manual contouring in terms of RMS distance. The introduced automatic segmentation algorithm is effective on different sequences of MR data sets without any parameter tuning. It requires no user interaction so variability introduced by manual tracing or interactive thresholding can be eliminated. Currently, the introduced segmentation algorithm is applied in the automated inter- and intra-modality image

  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. 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

  16. Recent technological advances in pediatric brain tumor surgery.

    PubMed

    Zebian, Bassel; Vergani, Francesco; Lavrador, José Pedro; Mukherjee, Soumya; Kitchen, William John; Stagno, Vita; Chamilos, Christos; Pettorini, Benedetta; Mallucci, Conor

    2017-01-01

    X-rays and ventriculograms were the first imaging modalities used to localize intracranial lesions including brain tumors as far back as the 1880s. Subsequent advances in preoperative radiological localization included computed tomography (CT; 1971) and MRI (1977). Since then, other imaging modalities have been developed for clinical application although none as pivotal as CT and MRI. Intraoperative technological advances include the microscope, which has allowed precise surgery under magnification and improved lighting, and the endoscope, which has improved the treatment of hydrocephalus and allowed biopsy and complete resection of intraventricular, pituitary and pineal region tumors through a minimally invasive approach. Neuronavigation, intraoperative MRI, CT and ultrasound have increased the ability of the neurosurgeon to perform safe and maximal tumor resection. This may be facilitated by the use of fluorescing agents, which help define the tumor margin, and intraoperative neurophysiological monitoring, which helps identify and protect eloquent brain.

  17. Thallium-201 SPECT imaging of brain tumors: Methods and results

    SciTech Connect

    Kim, K.T.; Black, K.L.; Marciano, D.; Mazziotta, J.C.; Guze, B.H.; Grafton, S.; Hawkins, R.A.; Becker, D.P. )

    1990-06-01

    Recent studies suggest that thallium-201 ({sup 201}Tl) planar scans of brain tumors more accurately reflect viable tumor burden than CT, MRI, or radionuclide studies with other single-photon emitting compounds. We have previously reported the utility of {sup 201}Tl SPECT index in distinguishing low- from high-grade gliomas elsewhere. Here we describe the technical considerations of deriving a simple {sup 201}Tl index, based on uptake in the tumor normalized to homologous contralateral tissue, from SPECT images of brain tumors. We evaluated the importance of consistently correcting for tissue attenuation, as it may achieve better lesion discrimination on qualitative inspection, and the methodologic limitations imposed by partial volume effects at the limits of resolution.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. FDOPA PET-CT of Nonenhancing Brain Tumors.

    PubMed

    Bund, Caroline; Heimburger, Céline; Imperiale, Alessio; Lhermitte, Benoît; Chenard, Marie-Pierre; Lefebvre, François; Kremer, Stéphane; Proust, François; Namer, Izzie-Jacques

    2017-04-01

    Primary brain tumor grading is crucial to rapidly determine the therapeutic impact and prognosis of a brain tumor as well as the tumors' aggressiveness profile. On magnetic resonance imaging, high-grade tumors are usually responsible for blood -brain barrier breakdowns, which result in tumor enhancement. However, this is not always the case. The main objective of this study was to evaluate the diagnostic value of FDOPA PET in the assessment of primary brain tumor aggressiveness with no contrast enhancement on MRI. Fifty-three patients were prospectively included: 35 low-grade and 18 high-grade histologically proven gliomas, with no contrast enhancement. Each patient underwent static PET acquisitions at 30 minutes. All patients had MRSI with measurements of different metabolites ratio. FDOPA was useful in the subgroup of low-grade gliomas, discriminating between dysembryoplastic neuroepithelial tumor and grade II oligodendroglioma (P < 0.01). An optimal threshold of the maximum standardized uptake value at 30 minutes (SUVmax (T/N)30) = 2.16 to discriminated low- from high-grade gliomas with a sensitivity of 60%, specificity of 100%, PPV of 100%, and NPV of 83.33% (P < 0.01). The nCho/Cr and nCho/NAA ratios were significantly higher in high- than in low-grade gliomas (P < 0.03 and P < 0.04, respectively). A significant positive correlation between MRSI ratios and SUVmax was found. Including data from amino acid metabolism used alone or in association with MRSI allows us to discriminate between dysembryoplastic neuroepithelial tumor and grade II oligodendroglioma and between low- and high-grade gliomas with no contrast enhancement on MRI.

  4. 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.

  5. Brain tumor surgery with 3-dimensional surface navigation.

    PubMed

    Mert, Ayguel; Buehler, Katja; Sutherland, Garnette R; Tomanek, Boguslaw; Widhalm, Georg; Kasprian, Gregor; Knosp, Engelbert; Wolfsberger, Stefan

    2012-12-01

    Precise lesion localization is necessary for neurosurgical procedures not only during the operative approach, but also during the preoperative planning phase. To evaluate the advantages of 3-dimensional (3-D) brain surface visualization over conventional 2-dimensional (2-D) magnetic resonance images for surgical planning and intraoperative guidance in brain tumor surgery. Preoperative 3-D brain surface visualization was performed with neurosurgical planning software in 77 cases (58 gliomas, 7 cavernomas, 6 meningiomas, and 6 metastasis). Direct intraoperative navigation on the 3-D brain surface was additionally performed in the last 20 cases with a neurosurgical navigation system. For brain surface reconstruction, patient-specific anatomy was obtained from MR imaging and brain volume was extracted with skull stripping or watershed algorithms, respectively. Three-dimensional visualization was performed by direct volume rendering in both systems. To assess the value of 3-D brain surface visualization for topographic lesion localization, a multiple-choice test was developed. To assess accuracy and reliability of 3-D brain surface visualization for intraoperative orientation, we topographically correlated superficial vessels and gyral anatomy on 3-D brain models with intraoperative images. The rate of correct lesion localization with 3-D was significantly higher (P = .001, χ), while being significantly less time consuming (P < .001, χ) compared with 2-D images. Intraoperatively, visual correlation was found between the 3-D images, superficial vessels, and gyral anatomy. The proposed method of 3-D brain surface visualization is fast, clinically reliable for preoperative anatomic lesion localization and patient-specific planning, and, together with navigation, improves intraoperative orientation in brain tumor surgery and is relatively independent of brain shift.

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

    PubMed

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

    2012-11-01

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

  7. 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

  8. 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.

  9. Brain tumors and epilepsy: pathophysiology of peritumoral changes.

    PubMed

    Shamji, Mohammed F; Fric-Shamji, Elana C; Benoit, Brien G

    2009-07-01

    Epilepsy commonly develops among patients with brain tumors, frequently even as the presenting symptom, and such patients consequently experience substantial morbidity from both the seizures and the underlying disease. At clinical presentation, these seizures are most commonly focal with secondary generalization and conventional medical management is often met with less efficacy. The molecular pathophysiology of these seizures is being elucidated with findings that both the tumoral and peritumoral microenvironments may exhibit epileptogenic phenotypes owing to disordered neuronal connectivity and regulation, impaired glial cell function, and the presence of altered vascular supply and permeability. Neoplastic tissue can itself be the initiation site of seizure activity, particularly for tumors arising from neuronal cell lines, such as gangliogliomas or dysembryoblastic neuroepithelial tumors. Conversely, a growing intracranial lesion can both structurally and functionally alter the surrounding brain tissue with edema, vascular insufficiency, inflammation, and release of metabolically active molecules, hence also promoting seizure activity. The involved mechanisms are certain to be multifactorial and depend on specific tumor histology, integrity of the blood brain barrier, and characteristics of the peritumoral environment. Understanding these changes that underlie tumor-related epilepsy may have roles in both optimal medical management for the seizure symptom and optimal surgical objective and management of the underlying disease.

  10. Development and characterization of a brain tumor mimicking fluorescence phantom

    NASA Astrophysics Data System (ADS)

    Haj-Hosseini, Neda; Kistler, Benjamin; Wârdell, Karin

    2014-03-01

    Fluorescence guidance using 5-aminolevulinic acid (5-ALA) for brain tumor resection is a recent technique applied to the highly malignant brain tumors. Five-ALA accumulates as protoporphyrin IX fluorophore in the tumor cells in different concentrations depending on the tumor environment and cell properties. Our group has developed a fluorescence spectroscopy system used with a hand-held probe intra-operatively. The system has shown improvement of fluorescence detection and allows quantification that preliminarily correlates with tumor malignancy grade during surgery. However, quantification of fluorescence is affected by several factors including the initial fluorophore concentration, photobleaching due to operating lamps and attenuation from the blood. Accordingly, an optical phantom was developed to enable controlled fluorescence measurements and evaluation of the system outside of the surgical procedure. The phantom mimicked the optical properties of glioma at the specific fluorescence excitation wavelength when different concentrations of the fluorophore were included in the phantom. To allow evaluation of photobleaching, kinetics of fluorophore molecules in the phantom was restricted by solidifying the phantoms. Moreover, a model for tissue autofluorescence was added. The fluorescence intensity's correlation with fluorophore concentration in addition to the photobleaching properties were investigated in the phantoms and were compared to the clinical data measured on the brain tumor.

  11. 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.

  12. 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.

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

    PubMed Central

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

    2005-01-01

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

  14. Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery.

    PubMed

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

    2005-04-01

    Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. Tumor volume range was 22,413-85,189 mm3, and range of number of vertices in triangulated models was 3674-6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 x 10(-5) mm(-1). Summary measures yielded a DSC range of 0.89-0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002-0.0028 (pooled median, 0.0005). 3D shapes of tumors may be approximated by using SH for neurosurgical applications.

  15. 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

  16. 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

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

    PubMed

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

    2015-11-07

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

  18. Objective and reproducible segmentation and quantification of tuberous sclerosis lesions in FLAIR brain MR images

    NASA Astrophysics Data System (ADS)

    Alderliesten, Tanja; Niessen, Wiro J.; Vincken, Koen L.; Maintz, J. B. Antoine; Jansen, Floor; van Nieuwenhuizen, Onno; Viergever, Max A.

    2001-07-01

    A semi-automatic segmentation method for Tuberous Sclerosis (TS) lesions in the brain has been developed. Both T1 images and Fluid Attenuated Inversion Recovery (FLAIR) images are integrated in the segmentation procedure. The segmentation procedure is mainly based on the notion of fuzzy connectedness. This approach uses the two basic concepts of adjacency and affinity to form a fuzzy relation between voxels in the image. The affinity is defined using two quantities that are both based on characteristics of the intensities in the lesion and surrounding brain tissue (grey and white matter). The semi-automatic method has been compared to results of manual segmentation. Manual segmentation is prone to interobserver and intraobserver variability. This was especially true for this particular study, where large variations were observed, which implies that a golden standard for comparison was not available. The method did perform within the variability of the observers and therefore has the potential to improve reproducibility of quantitative measurements.

  19. 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

  20. 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

  1. 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

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

    PubMed

    Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L; Rueckert, Daniel; Thiran, Jean-Philippe; Bach Cuadra, Meritxell; Sznitman, Raphael

    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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. Affective Symptoms and White Matter Changes in Brain Tumor Patients.

    PubMed

    Richter, Andre; Woernle, Cristoph M; Krayenbühl, Niklaus; Kollias, Spyridon; Bellut, David

    2015-10-01

    Affective symptoms are frequent in patients with brain tumors. The origin of such symptoms is unknown; either focal brain injury or reactive emotional distress may be responsible. This cross-sectional pilot study linked depressive symptoms and anxiety to white matter integrity. The objective was to test the hypothesis of a relationship between tissue damage and brain function in patients with brain tumors and to provide a basis for further studies in this field. Diffusion tensor imaging was performed in 39 patients with newly diagnosed supratentorial primary brain tumor. Patients completed the Beck Depression Inventory, and examiners rated them on the Hamilton Depression Rating Scale (HDRS). State and trait anxiety were measured using the State-Trait Anxiety Inventory. Correlations between fractional anisotropy (FA) and psychological measures were assessed on the basis of regions of interest; the defined regions of interest corresponded to clearly specified white matter tracts. Statistical analysis revealed correlations between FA in the left internal capsule and scores on the HDRS, Beck Depression Inventory, and State-Trait Anxiety Inventory (P < 0.05). HDRS scores were also correlated with FA in the right medial uncinate fasciculus, and state anxiety scores were significantly correlated with FA in the left lateral and medial uncinate fasciculus (P < 0.05). Our results suggest that neurobiologic mechanisms related to the integrity of tissue in specific white matter tracts may influence affective symptoms in patients with brain tumors, and these mechanisms can be investigated with diffusion tensor imaging. However, prospective observational studies are needed to investigate further the links between brain structures and the severity of affective symptoms in this patient population. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. 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

  11. 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.

  12. Brain MR image segmentation improved algorithm based on probability

    NASA Astrophysics Data System (ADS)

    Liao, Hengxu; Liu, Gang; Guo, Xiantang

    2017-08-01

    Local weight voting algorithm is a kind of current mainstream segmentation algorithm. It takes full account of the influences of the likelihood of image likelihood and the prior probabilities of labels on the segmentation results. But this method still can be improved since the essence of this method is to get the label with the maximum probability. If the probability of a label is 70%, it may be acceptable in mathematics. But in the actual segmentation, it may be wrong. So we use the matrix completion algorithm as a supplement. When the probability of the former is larger, the result of the former algorithm is adopted. When the probability of the later is larger, the result of the later algorithm is adopted. This is equivalent to adding an automatic algorithm selection switch that can theoretically ensure that the accuracy of the algorithm we propose is superior to the local weight voting algorithm. At the same time, we propose an improved matrix completion algorithm based on enumeration method. In addition, this paper also uses a multi-parameter registration model to reduce the influence that the registration made on the segmentation. The experimental results show that the accuracy of the algorithm is better than the common segmentation algorithm.

  13. 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…

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. Applications of Ultrasound in the Resection of Brain Tumors.

    PubMed

    Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve; Frisken, Sarah; Kapur, Tina; Wells, William; Golby, Alexandra J

    2017-01-01

    Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies. Copyright © 2016 by the American Society of Neuroimaging.

  3. Automated delineation of brain structures in patients undergoing radiotherapy for primary brain tumors: from atlas to dose-volume histograms.

    PubMed

    Conson, Manuel; Cella, Laura; Pacelli, Roberto; Comerci, Marco; Liuzzi, Raffaele; Salvatore, Marco; Quarantelli, Mario

    2014-09-01

    To implement and evaluate a magnetic resonance imaging atlas-based automated segmentation (MRI-ABAS) procedure for cortical and sub-cortical grey matter areas definition, suitable for dose-distribution analyses in brain tumor patients undergoing radiotherapy (RT). 3T-MRI scans performed before RT in ten brain tumor patients were used. The MRI-ABAS procedure consists of grey matter classification and atlas-based regions of interest definition. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm was applied to structures manually delineated by four experts to generate the standard reference. Performance was assessed comparing multiple geometrical metrics (including Dice Similarity Coefficient - DSC). Dosimetric parameters from dose-volume-histograms were also generated and compared. Compared with manual delineation, MRI-ABAS showed excellent reproducibility [median DSCABAS=1 (95% CI, 0.97-1.0) vs. DSCMANUAL=0.90 (0.73-0.98)], acceptable accuracy [DSCABAS=0.81 (0.68-0.94) vs. DSCMANUAL=0.90 (0.76-0.98)], and an overall 90% reduction in delineation time. Dosimetric parameters obtained using MRI-ABAS were comparable with those obtained by manual contouring. The speed, reproducibility, and robustness of the process make MRI-ABAS a valuable tool for investigating radiation dose-volume effects in non-target brain structures providing additional standardized data without additional time-consuming procedures. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

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

    NASA Astrophysics Data System (ADS)

    Wu, Xuqiang; Bricq, Stéphanie; Collet, Christophe

    2011-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1997-05-01

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

  11. Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil.

    PubMed

    Shi, Feng; Yap, Pew-Thian; Fan, Yong; Cheng, Jie-Zhi; Wald, Lawrence L; Gerig, Guido; Lin, Weili; Shen, Dinggang

    2009-01-01

    The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods.

  12. 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

  13. 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

  14. 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.

  15. 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

  16. Expression of Cancer-Testis Genes in Brain Tumors

    PubMed Central

    Lee, Myoung-Hee; Kim, Ealmaan; Kim, In-Soo; Yim, Man-Bin; Kim, Sang-Pyo

    2008-01-01

    Objective Cancer-testis (CT) genes are considered promising candidates for immunotherapeutic approaches. The aim of this study was to investigate which CT genes should be targeted in immunotherapy for brain tumors. Methods We investigated the expression of 6 CT genes (MAGE-E1, SOX-6, SCP-1, SSX-2, SSX-4, and HOM-TES-85) using reverse-transcription polymerase chain reaction in 26 meningiomas and 32 other various brain tumor specimens, obtained from the patients during tumor surgery from 2000 to 2005. Results The most frequently expressed CT genes of meningiomas were MAGE-E1, which were found in 22/26 (85%) meningioma samples, followed by SOX-6 (9/26 or 35%). Glioblastomas were most frequently expressed SOX-6 (6/7 or 86%), MAGE-E1 (5/7 or 71%), followed by SSX-2 (2/7 or 29%) and SCP-1 (1/7 or 14%). However, 4 astrocytomas, 3 anaplastic astrocytomas, and 3 oligodendroglial tumors only expressed MAGE-E1 and SOX-6. Schwannomas also expressed SOX-6 (5/6 or 83%), MAGE-E1 (4/6 or 67%), and SCP-1 (2/6 or 33%). Conclusion The data presented here suggest that MAGE-E1 and SOX-6 genes are expressed in a high percentage of human central nervous system tumors, which implies the CT genes could be the potential targets of immunotherapy for human central nervous system tumors. PMID:19096642

  17. 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.

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

    PubMed

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

    2015-04-01

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

  19. Segmentation of Intensity Inhomogeneous Brain MR Images Using Active Contours

    PubMed Central

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

    2014-01-01

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

  20. 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.

  1. 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.

  2. 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.

  3. Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

    NASA Astrophysics Data System (ADS)

    Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

    2017-09-01

    Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

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

    PubMed

    Goo, Hyun Woo; 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.

  5. 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 pedi