Science.gov

Sample records for brain tumor segmentation

  1. Automatic brain tumor segmentation

    NASA Astrophysics Data System (ADS)

    Clark, Matthew C.; Hall, Lawrence O.; Goldgof, Dmitry B.; Velthuizen, Robert P.; Murtaugh, F. R.; Silbiger, Martin L.

    1998-06-01

    A system that automatically segments and labels complete glioblastoma-multiform tumor volumes in magnetic resonance images of the human brain is presented. The magnetic resonance images consist of three feature images (T1- weighted, proton density, T2-weighted) and are processed by a system which integrates knowledge-based techniques with multispectral analysis and is independent of a particular magnetic resonance scanning protocol. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intra-cranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intra-cranial region, with region analysis used in performing the final tumor labeling. This system has been trained on eleven volume data sets and tested on twenty-two unseen volume data sets acquired from a single magnetic resonance imaging system. The knowledge-based tumor segmentation was compared with radiologist-verified `ground truth' tumor volumes and results generated by a supervised fuzzy clustering algorithm. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

  2. 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. PMID:23790354

  3. 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.

  4. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

    PubMed

    Zhao, Liya; Jia, Kebin

    2016-01-01

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

  5. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

    PubMed Central

    Zhao, Liya; Jia, Kebin

    2016-01-01

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

  6. 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. PMID:18450536

  7. Multiresolution texture models for brain tumor segmentation in MRI.

    PubMed

    Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

    2011-01-01

    In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features. PMID:22255946

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

    PubMed

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

    2015-10-01

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

  9. Confidence-based ensemble for GBM brain tumor segmentation

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  10. Synthetic ground truth for validation of brain tumor MRI segmentation.

    PubMed

    Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido

    2005-01-01

    Validation and method of comparison for segmentation of magnetic resonance images (MRI) presenting pathology is a challenging task due to the lack of reliable ground truth. We propose a new method for generating synthetic multi-modal 3D brain MRI with tumor and edema, along with the ground truth. Tumor mass effect is modeled using a biomechanical model, while tumor and edema infiltration is modeled as a reaction-diffusion process that is guided by a modified diffusion tensor MRI. We propose the use of warping and geodesic interpolation on the diffusion tensors to simulate the displacement and the destruction of the white matter fibers. We also model the process where the contrast agent tends to accumulate in cortical csf regions and active tumor regions to obtain contrast enhanced T1w MR image that appear realistic. The result is simulated multi-modal MRI with ground truth available as sets of probability maps. The system will be able to generate large sets of simulation images with tumors of varying size, shape and location, and will additionally generate infiltrated and deformed healthy tissue probabilities. PMID:16685825

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

    NASA Astrophysics Data System (ADS)

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

    1998-06-01

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

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

    PubMed

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

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods. PMID:26351901

  13. 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.

  14. 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.

  15. Simulation of brain tumors in MR images for evaluation of segmentation efficacy.

    PubMed

    Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido

    2009-04-01

    Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors). PMID:19119055

  16. 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.

  17. 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.

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Demirhan, Ayşe; Toru, Mustafa; Guler, Inan

    2015-07-01

    Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema. PMID:25265636

  1. 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. PMID:21995070

  2. 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.

  3. 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.

  4. 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

  5. Brain Tumors

    MedlinePlus

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

  6. Brain Tumors

    MedlinePlus

    ... brain. Brain tumors can be benign, with no cancer cells, or malignant, with cancer cells that grow quickly. Some are primary brain ... targeted therapy. Targeted therapy uses substances that attack cancer cells without harming normal cells. Many people get ...

  7. Brain tumors.

    PubMed Central

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

    1991-01-01

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

  8. 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, ...

  9. Brain tumor - children

    MedlinePlus

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

  10. 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 ...

  11. 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 ...

  12. 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.

  13. Brain Tumors (For Parents)

    MedlinePlus

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

  14. Brain tumor (image)

    MedlinePlus

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

  15. 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 ...

  16. Adolescent and Pediatric Brain Tumors

    MedlinePlus

    ... abta.org Donate Now Menu Adolescent & Pediatric Brain Tumors Brain Tumors In Children Pediatric Brain Tumor Diagnosis Family ... or Complete our contact form Adolescent & Pediatric Brain Tumors Brain Tumors In Children Pediatric Brain Tumor Diagnosis Family ...

  17. Brain Tumor Symptoms

    MedlinePlus

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

  18. Metastatic brain tumor

    MedlinePlus

    ... brain from an unknown location. This is called cancer of unknown primary (CUP) origin. Growing brain tumors can place pressure ... not know the original location. This is called cancer of unknown primary (CUP) origin. Metastatic brain tumors occur in about ...

  19. 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 ...

  20. Pediatric Brain Tumor Foundation

    MedlinePlus

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

  1. 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, ...

  2. Brain Tumor Statistics

    MedlinePlus

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

  3. American Brain Tumor Association

    MedlinePlus

    ... 800-886-ABTA (2282) or Complete our contact form The American Brain Tumor Association was the first and is the only national organization committed to funding brain tumor research and providing ...

  4. Brain and Spinal Tumors

    MedlinePlus

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

  5. Childhood Brain Tumors

    MedlinePlus

    ... They are among the most common types of childhood cancers. Some are benign tumors, which aren't ... can still be serious. Malignant tumors are cancerous. Childhood brain and spinal cord tumors can cause headaches ...

  6. Brain tumor - children

    MedlinePlus

    Glioblastoma multiforme - children; Ependymoma - children; Glioma - children; Astrocytoma - children; Medulloblastoma - children; Neuroglioma - children; Oligodendroglioma - children; Meningioma - children; Cancer - brain tumor (children)

  7. Radioresistance of Brain Tumors

    PubMed Central

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

    2016-01-01

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

  8. Modern Brain Tumor Imaging

    PubMed Central

    Barajas, Ramon F.; Cha, Soonmee

    2015-01-01

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

  9. 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. PMID:17281898

  10. Epilepsy and brain tumors.

    PubMed

    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

  11. Brain tumor stem cells.

    PubMed

    Palm, Thomas; Schwamborn, Jens C

    2010-06-01

    Since the end of the 'no-new-neuron' theory, emerging evidence from multiple studies has supported the existence of stem cells in neurogenic areas of the adult brain. Along with this discovery, neural stem cells became candidate cells being at the origin of brain tumors. In fact, it has been demonstrated that molecular mechanisms controlling self-renewal and differentiation are shared between brain tumor stem cells and neural stem cells and that corruption of genes implicated in these pathways can direct tumor growth. In this regard, future anticancer approaches could be inspired by uncovering such redundancies and setting up treatments leading to exhaustion of the cancer stem cell pool. However, deleterious effects on (normal) neural stem cells should be minimized. Such therapeutic models underline the importance to study the cellular mechanisms implicated in fate decisions of neural stem cells and the oncogenic derivation of adult brain cells. In this review, we discuss the putative origins of brain tumor stem cells and their possible implications on future therapies. PMID:20370314

  12. Aquaporins and Brain Tumors.

    PubMed

    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

  13. 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

  14. Brain Tumor Risk Factors

    MedlinePlus

    ... to the genes can be called “genetic.” However, only about 5 to 10 percent of all cancer is hereditary (ie, passed down from one generation to another in a family). In cases of hereditary brain tumors, a mutation, or change in the DNA ...

  15. Living with a Brain Tumor

    MedlinePlus

    ... Mentor The ABTA's Online Support Community Understanding The Affordable Care Act Living with a Brain Tumor Understanding Emotions Talking ... Mentor The ABTA's Online Support Community Understanding The Affordable Care Act Living with a Brain Tumor Understanding Emotions Talking ...

  16. 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) ...

  17. 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

  18. Automatic tumor segmentation using knowledge-based techniques.

    PubMed

    Clark, M C; Hall, L O; Goldgof, D B; Velthuizen, R; Murtagh, F R; Silbiger, M S

    1998-04-01

    A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised k-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time. PMID:9688151

  19. Brain Tumor Epidemiology Consortium (BTEC)

    Cancer.gov

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

  20. Intra-axial brain tumors.

    PubMed

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

    2016-01-01

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

  1. [Imaging of childhood brain tumors].

    PubMed

    Couanet, D; Adamsbaum, C

    2006-06-01

    Brain tumors represent around a quarter of all solid tumors observed in the pediatric population. Infratentorial tumors are the most frequent, mostly encountered between 4 and 11 years of age. Early imaging is important because initial symptoms can be misinterpreted as statural and pubertal disorders or pseudoabdominal symptoms with apathy and vomiting in infants. Because signal abnormalities on MRI are most often not specific, it is essential to take into account the clinical and topographic characteristics of the lesion to establish an appropriate differential diagnosis. The main patterns of brain tumors observed in pediatrics are presented. Brain metastases are very unusual in children, in contrast to lepto-meningeal metastasis. PMID:16778744

  2. 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

  3. 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

  4. Pediatric Brain Tumors: An Update.

    PubMed

    Segal, Devorah; Karajannis, Matthias A

    2016-07-01

    Brain tumors collectively represent the most common solid tumors in childhood and account for significant morbidity and mortality. Until recently, pediatric brain tumors were diagnosed and classified solely based on histologic criteria, and treatments were chosen empirically. Recent research has greatly enhanced our understanding of the diverse biology of pediatric brain tumors, their molecular and genetic underpinnings, leading to improved diagnostic accuracy and risk stratification, as well as the development of novel biomarkers and molecular targeted therapies. For subsets of patients, these new treatment options have already resulted in improved survival and decreased treatment toxicity. In this article, we provide an overview of the most common childhood brain tumors, describe recent key advances in the field, and discuss the therapeutic challenges that remain. PMID:27230809

  5. Radiosurgery for Pediatric Brain Tumors.

    PubMed

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

    2016-03-01

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

  6. 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.

  7. Dendrimer technologies for brain tumor.

    PubMed

    Mishra, Vijay; Kesharwani, Prashant

    2016-05-01

    Despite low prevalence, brain tumors are one of the most lethal forms of cancer. Unfortunately the blood-brain barrier (BBB), a highly regulated, well coordinated and efficient barrier, checks the permeation of most of the drugs across it. Hence, crossing this barrier is one of the most significant challenges in the development of efficient central nervous system therapeutics. Surface-engineered dendrimers improve biocompatibility, drug-release kinetics and aptitude to target the BBB and/or tumors and facilitate transportation of anticancer bioactives across the BBB. This review sheds light on different aspects of brain tumors and dendrimers based on different approaches for treatment including recent research, opportunities and challenges encountered in development of novel and efficient dendrimer-based therapeutics for the treatment of brain tumors. PMID:26891979

  8. Segmentation of liver region with tumorous tissues

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

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

  9. Brain and Spinal Cord Tumors in Adults

    MedlinePlus

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

  10. 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. PMID:26739264

  11. 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.

  12. Extra-axial brain tumors.

    PubMed

    Rapalino, Otto; Smirniotopoulos, James G

    2016-01-01

    Extra-axial brain tumors are the most common adult intracranial neoplasms and encompass a broad spectrum of pathologic subtypes. Meningiomas are the most common extra-axial brain tumor (approximately one-third of all intracranial neoplasms) and typically present as slowly growing dural-based masses. Benign meningiomas are very common, and may occasionally be difficult to differentiate from more aggressive subtypes (i.e., atypical or malignant varieties) or other dural-based masses with more aggressive biologic behavior (e.g., hemangiopericytoma or dural-based metastases). Many neoplasms that typically affect the brain parenchyma (intra-axial), such as gliomas, may also present with primary or secondary extra-axial involvement. This chapter provides a general and concise overview of the common types of extra-axial tumors and their typical imaging features. PMID:27432671

  13. 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.

  14. Evaluation of atlas based mouse brain segmentation

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

    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.

  15. 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.

  16. 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

  17. The kynurenine pathway in brain tumor pathogenesis.

    PubMed

    Adams, Seray; Braidy, Nady; Bessede, Alban; Bessesde, Alban; Brew, Bruce J; Grant, Ross; Teo, Charlie; Guillemin, Gilles J

    2012-11-15

    Brain tumors are among the most common and most chemoresistant tumors. Despite treatment with aggressive treatment strategies, the prognosis for patients harboring malignant gliomas remains dismal. The kynurenine pathway (KP) is the principal route of L-tryptophan catabolism leading to the formation of the essential pyridine nucleotide, nicotinamide adenine dinucleotide (NAD(+)), and important neuroactive metabolites, including the neurotoxin, quinolinic acid (QUIN), the neuroprotective agent, picolinic acid (PIC), the T(H)17/Treg balance modulator, 3-hydroxyanthranilic acid (3-HAA), and the immunosuppressive agent, L-kynurenine (KYN). This review provides a new perspective on KP dysregulation in defeating antitumor immune responses, specifically bringing light to the lower segment of the KP, particularly QUIN-induced neurotoxicity and downregulation of the enzyme α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (ACMSD) as a potential mechanism of tumor progression. Given its immunosuppressive effects, 3-HAA produced from the KP may also play a role in suppressing antitumor immunity in human tumors. The enzyme indoleamine 2, 3-dioxygenase (IDO-1) initiates and regulates the first step of the KP in most cells. Mounting evidence directly implicates that the induction and overexpression of IDO-1 in various tumors is a crucial mechanism facilitating tumor immune evasion and persistence. Tryptophan 2, 3-dioxygenase (TDO-2), which initiates the same first step of the KP as IDO-1, has likewise recently been shown to be a mechanism of tumoral immune resistance. Further, it was also recently shown that TDO-2-dependent production of KYN by brain tumors might be a novel mechanism for suppressing antitumor immunity and supporting tumor growth through the activation of the Aryl hydrocarbon receptor (AhR). This newly identified TDO-2-KYN-AhR signaling pathway opens up exciting future research opportunities and may represent a novel therapeutic target in cancer therapy

  18. 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

  19. Automatic brain segmentation in rhesus monkeys

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

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

  20. Multiple glomus tumors and segmental neurofibromatosis: there are no coincidences.

    PubMed

    Cabral, Rita; Santiago, F; Tellechea, O

    2011-01-01

    Segmental neurofibromatosis is a rare subtype of neurofibromatosis type 1 (NF1). Glomus tumors are uncommon benign tumors. The authors report the association between these two rare conditions, not yet reported. PMID:21426870

  1. 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

  2. Deregulated proliferation and differentiation in brain tumors.

    PubMed

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

    2015-01-01

    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

  3. Primary Neuroendocrine Tumor in Brain

    PubMed Central

    Tamura, Ryota; Kuroshima, Yoshiaki; Nakamura, Yoshiki

    2014-01-01

    The incidence of brain metastases for neuroendocrine tumor (NET) is reportedly 1.5~5%, and the origin is usually pulmonary. A 77-year-old man presented to our hospital with headache and disturbance of specific skilled motor activities. Computed tomography (CT) showed a massive neoplastic lesion originating in the left temporal and parietal lobes that caused a mass edematous effect. Grossly, total resection of the tumor was achieved. Histological examination revealed much nuclear atypia and mitotic figures. Staining for CD56, chromogranin A, and synaptophysin was positive, indicating NET. The MIB-1 index was 37%. Histopathologically, the tumor was diagnosed as NET. After surgery, gastroscopy and colonoscopy were performed, but the origin was not seen. After discharge, CT and FDG-PET (fluoro-2-deoxy-d-glucose positron emission tomography) were performed every 3 months. Two years later we have not determined the origin of the tumor. It is possible that the brain is the primary site of this NET. To our knowledge, this is the first reported case of this phenomenon. PMID:25506006

  4. Antiangiogenic therapy in brain tumors

    PubMed Central

    Lakka, Sajani S; Rao, Jasti S

    2008-01-01

    Angiogenesis, the recruitment of new blood vessels, is an essential component of tumor progression. Malignant brain tumors are highly vascularized and their growth is angiogenesis-dependent. As such, inhibition of the sprouting of new capillaries from pre-existing blood vessels is one of the most promising antiglioma therapeutic approaches. Numerous classes of molecules have been implicated in regulating angiogenesis and, thus, novel agents that target and counteract angiogenesis are now being developed. The therapeutic trials of a number of angiogenesis inhibitors as antiglioma drugs are currently under intense investigation. Preliminary studies of angiogenic blockade in glioblastoma have been promising and several clinical trials are now underway to develop optimum treatment strategies for antiangiogenic agents. This review will cover state-of-the-art antiangiogenic targets for brain tumor treatment and discuss future challenges. An increased understanding of the angiogenic process, the diversity of its inducers and mediators, appropriate drug schedules and the use of these agents with other modalities may lead to radically new treatment regimens to achieve maximal efficacy. PMID:18928341

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

    PubMed

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

    2009-07-01

    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

  6. Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

    NASA Astrophysics Data System (ADS)

    Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

    2008-03-01

    In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

  7. Automated brain segmentation using neural networks

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  8. 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.

  9. Classification of brain tumors using MRI and MRS data

    NASA Astrophysics Data System (ADS)

    Wang, Qiang; Liacouras, Eirini Karamani; Miranda, Erickson; Kanamalla, Uday S.; Megalooikonomou, Vasileios

    2007-03-01

    We study the problem of classifying brain tumors as benign or malignant using information from magnetic resonance (MR) imaging and magnetic resonance spectroscopy (MRS) to assist in clinical diagnosis. The proposed approach consists of several steps including segmentation, feature extraction, feature selection, and classification model construction. Using an automated segmentation technique based on fuzzy connectedness we accurately outline the tumor mass boundaries in the MR images so that further analysis concentrates on these regions of interest (ROIs). We then apply a concentric circle technique on the ROIs to extract features that are utilized by the classification algorithms. To remove redundant features, we perform feature selection where only those features with discriminatory information (among classes) are used in the model building process. The involvement of MRS features further improves the classification accuracy of the model. Experimental results demonstrate the effectiveness of the proposed approach in classifying brain tumors in MR images.

  10. Interstitial irradiation of brain tumors: a review

    SciTech Connect

    Bernstein, M.; Gutin, P.H.

    1981-12-01

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

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

    PubMed Central

    Despotović, Ivana

    2015-01-01

    Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. PMID:25945121

  12. A Unified Framework for Brain Segmentation in MR Images.

    PubMed

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

    2015-01-01

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

  13. 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

  14. 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.

  15. Radiosensitized treatment of malignant brain tumors

    NASA Astrophysics Data System (ADS)

    Bloznelyte-Plesniene, Laima

    2003-12-01

    Around 12,000 deaths from glioblastoma occurs within the European Community annually. At present, the best available treatment for malignant brain tumors results in a median survival of patients of 15 months despite surgery, radiotherapy, and chemotherapy. The purpose of this paper is to review our results of radiosensitized treatment of malignant brain tumors.

  16. 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

  17. Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

    PubMed Central

    Zhu, Y; Young, G; Xue, Z; Huang, R; You, H; Setayesh, K; Hatabu, H; Cao, F; Wong, S.T.

    2012-01-01

    Rationale and Objectives Quantitative measurement provides essential information about disease progression and treatment response in patients with Glioblastoma multiforme (GBM). The goal of this paper is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted MR brain data, and the latter refines the segmentation results with minimal manual input. Results Twenty six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface (GUI). Conclusion Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MRI data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology. PMID:22591720

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

    MedlinePlus

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

  19. 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

  20. Combining multi-atlas segmentation with brain surface estimation

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  1. Brain tumors at a nuclear facility.

    PubMed

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

    1984-10-01

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

  2. Malignant metastatic carcinoid presenting as brain tumor

    PubMed Central

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

    2016-01-01

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

  3. Malignant metastatic carcinoid presenting as brain tumor.

    PubMed

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

    2016-01-01

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

  4. 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.

  5. Intraoperative infrared imaging of brain tumors

    PubMed Central

    Gorbach, Alexander M.; Heiss, John D.; Kopylev, Leonid; Oldfield, Edward H.

    2014-01-01

    Object Although clinical imaging defines the anatomical relationship between a brain tumor and the surrounding brain and neurological deficits indicate the neurophysiological consequences of the tumor, the effect of a brain tumor on vascular physiology is less clear. Methods An infrared camera was used to measure the temperature of the cortical surface before, during, and after removal of a mass in 34 patients (primary brain tumor in 21 patients, brain metastases in 10 and falx meningioma, cavernous angioma, and radiation necrosis–astrocytosis in one patient each). To establish the magnitude of the effect on blood flow induced by the tumor, the images were compared with those from a group of six patients who underwent temporal lobectomy for epilepsy. In four cases a cerebral artery was temporarily occluded during the course of the surgery and infrared emissions from the cortex before and after occlusion were compared to establish the relationship of local temperature to regional blood flow. Discrete temperature gradients were associated with surgically verified lesions in all cases. Depending on the type of tumor, the cortex overlying the tumor was either colder or warmer than the surrounding cortex. Spatial reorganization of thermal gradients was observed after tumor resection. Temperature gradients of the cortex in patients with tumors exceeded those measured in the cortex of patients who underwent epilepsy surgery. Conclusions Brain tumors induce changes in cerebral blood flow (CBF) in the cortex, which can be made visible by performing infrared imaging during cranial surgery. A reduction in CBF beyond the tumor margin improves after removal of the lesion. PMID:15599965

  6. Brain Tumor Epidemiology Consortium Membership Information

    Cancer.gov

    BTEC welcomes new members interested in the development of multi-center, inter-disciplinary collaborations that will lead to a better understanding of the etiology, outcomes and prevention of all brain tumors.

  7. Brain Tumors - Multiple Languages: MedlinePlus

    MedlinePlus

    ... Supplements Videos & Tools You Are Here: Home → Multiple Languages → All Health Topics → Brain Tumors URL of this page: https://www.nlm.nih.gov/medlineplus/languages/braintumors.html Other topics A-Z A B ...

  8. Staging Childhood Brain and Spinal Cord Tumors

    MedlinePlus

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

  9. 3-D in vivo brain tumor geometry study by scaling analysis

    NASA Astrophysics Data System (ADS)

    Torres Hoyos, F.; Martín-Landrove, M.

    2012-02-01

    A new method, based on scaling analysis, is used to calculate fractal dimension and local roughness exponents to characterize in vivo 3-D tumor growth in the brain. Image acquisition was made according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal magnetic resonance T1-weighted images, and comprising the brain volume for image registration. Image segmentation was performed by the application of the k-means procedure upon contrasted images. We analyzed glioblastomas, astrocytomas, metastases and benign brain tumors. The results show significant variations of the parameters depending on the tumor stage and histological origin.

  10. 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.

  11. 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.

  12. Segmentation of ultrasonic breast tumors based on homogeneous patch

    PubMed Central

    Gao, Liang; Yang, Wei; Liao, Zhiwu; Liu, Xiaoyun; Feng, Qianjin; Chen, Wufan

    2012-01-01

    Purpose: Accurately segmenting breast tumors in ultrasound (US) images is a difficult problem due to their specular nature and appearance of sonographic tumors. The current paper presents a variant of the normalized cut (NCut) algorithm based on homogeneous patches (HP-NCut) for the segmentation of ultrasonic breast tumors. Methods: A novel boundary-detection function is defined by combining texture and intensity information to find the fuzzy boundaries in US images. Subsequently, based on the precalculated boundary map, an adaptive neighborhood according to image location referred to as a homogeneous patch (HP) is proposed. HPs are guaranteed to spread within the same tissue region; thus, the statistics of primary features within the HPs is more reliable in distinguishing the different tissues and benefits subsequent segmentation. Finally, the fuzzy distribution of textons within HPs is used as final image features, and the segmentation is obtained using the NCut framework. Results: The HP-NCut algorithm was evaluated on a large dataset of 100 breast US images (50 benign and 50 malignant). The mean Hausdorff distance measure, the mean minimum Euclidean distance measure and similarity measure achieved 7.1 pixels, 1.58 pixels, and 86.67%, respectively, for benign tumors while those achieved 10.57 pixels, 1.98 pixels, and 84.41%, respectively, for malignant tumors. Conclusions: The HP-NCut algorithm provided the improvement in accuracy and robustness compared with state-of-the-art methods. A conclusion that the HP-NCut algorithm is suitable for ultrasonic tumor segmentation problems can be drawn. PMID:22755713

  13. General Information about Childhood Brain and Spinal Cord Tumors

    MedlinePlus

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

  14. Proton MRS imaging in pediatric brain tumors.

    PubMed

    Zarifi, Maria; Tzika, A Aria

    2016-06-01

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

  15. 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. PMID:25059388

  16. Visual analysis of longitudinal brain tumor perfusion

    NASA Astrophysics Data System (ADS)

    Glaßer, Sylvia; Oeltze, Steffen; Preim, Uta; Bjørnerud, Atle; Hauser, Helwig; Preim, Bernhard

    2013-02-01

    In clinical research on diagnosis and evaluation of brain tumors, longitudinal perfusion MRI studies are acquired for tumor grading as well as to monitor and assess treatment response and patient prognosis. Within this work, we demonstrate how visual analysis techniques can be adapted to multidimensional datasets from such studies within a framework to support the computer-aided diagnosis of brain tumors. Our solution builds on two innovations: First, we introduce a pipeline yielding comparative, co-registered quantitative perfusion parameter maps over all time steps of the longitudinal study. Second, based on these time-dependent parameter maps, visual analysis methods were developed and adapted to reveal valuable insight into tumor progression, especially regarding the clinical research area of low grade glioma transformation into high grade gliomas. Our examination of four longitudinal brain studies demonstrates the suitability of the presented visual analysis methods and comprises new possibilities for the clinical researcher to characterize the development of low grade gliomas.

  17. Ambient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors.

    PubMed

    Eberlin, Livia S; Norton, Isaiah; Orringer, Daniel; Dunn, Ian F; Liu, Xiaohui; Ide, Jennifer L; Jarmusch, Alan K; Ligon, Keith L; Jolesz, Ferenc A; Golby, Alexandra J; Santagata, Sandro; Agar, Nathalie Y R; Cooks, R Graham

    2013-01-29

    The main goal of brain tumor surgery is to maximize tumor resection while preserving brain function. However, existing imaging and surgical techniques do not offer the molecular information needed to delineate tumor boundaries. We have developed a system to rapidly analyze and classify brain tumors based on lipid information acquired by desorption electrospray ionization mass spectrometry (DESI-MS). In this study, a classifier was built to discriminate gliomas and meningiomas based on 36 glioma and 19 meningioma samples. The classifier was tested and results were validated for intraoperative use by analyzing and diagnosing tissue sections from 32 surgical specimens obtained from five research subjects who underwent brain tumor resection. The samples analyzed included oligodendroglioma, astrocytoma, and meningioma tumors of different histological grades and tumor cell concentrations. The molecular diagnosis derived from mass-spectrometry imaging corresponded to histopathology diagnosis with very few exceptions. Our work demonstrates that DESI-MS technology has the potential to identify the histology type of brain tumors. It provides information on glioma grade and, most importantly, may help define tumor margins by measuring the tumor cell concentration in a specimen. Results for stereotactically registered samples were correlated to preoperative MRI through neuronavigation, and visualized over segmented 3D MRI tumor volume reconstruction. Our findings demonstrate the potential of ambient mass spectrometry to guide brain tumor surgery by providing rapid diagnosis, and tumor margin assessment in near-real time. PMID:23300285

  18. Automatic Segmentation of Eight Tissue Classes in Neonatal Brain MRI

    PubMed Central

    Anbeek, Petronella; Išgum, Ivana; van Kooij, Britt J. M.; Mol, Christian P.; Kersbergen, Karina J.; Groenendaal, Floris; Viergever, Max A.; de Vries, Linda S.; Benders, Manon J. N. L.

    2013-01-01

    Purpose Volumetric measurements of neonatal brain tissues may be used as a biomarker for later neurodevelopmental outcome. We propose an automatic method for probabilistic brain segmentation in neonatal MRIs. Materials and Methods In an IRB-approved study axial T1- and T2-weighted MR images were acquired at term-equivalent age for a preterm cohort of 108 neonates. A method for automatic probabilistic segmentation of the images into eight cerebral tissue classes was developed: cortical and central grey matter, unmyelinated and myelinated white matter, cerebrospinal fluid in the ventricles and in the extra cerebral space, brainstem and cerebellum. Segmentation is based on supervised pixel classification using intensity values and spatial positions of the image voxels. The method was trained and evaluated using leave-one-out experiments on seven images, for which an expert had set a reference standard manually. Subsequently, the method was applied to the remaining 101 scans, and the resulting segmentations were evaluated visually by three experts. Finally, volumes of the eight segmented tissue classes were determined for each patient. Results The Dice similarity coefficients of the segmented tissue classes, except myelinated white matter, ranged from 0.75 to 0.92. Myelinated white matter was difficult to segment and the achieved Dice coefficient was 0.47. Visual analysis of the results demonstrated accurate segmentations of the eight tissue classes. The probabilistic segmentation method produced volumes that compared favorably with the reference standard. Conclusion The proposed method provides accurate segmentation of neonatal brain MR images into all given tissue classes, except myelinated white matter. This is the one of the first methods that distinguishes cerebrospinal fluid in the ventricles from cerebrospinal fluid in the extracerebral space. This method might be helpful in predicting neurodevelopmental outcome and useful for evaluating neuroprotective clinical

  19. Recent developments in brain tumor predisposing syndromes.

    PubMed

    Johansson, Gunnar; Andersson, Ulrika; Melin, Beatrice

    2016-01-01

    The etiologies of brain tumors are in the most cases unknown, but improvements in genetics and DNA screening have helped to identify a wide range of brain tumor predisposition disorders. In this review we are discussing some of the most common predisposition disorders, namely: neurofibromatosis type 1 and 2, schwannomatosis, rhabdoid tumor predisposition disorder, nevoid basal cell carcinoma syndrome (Gorlin), tuberous sclerosis complex, von Hippel-Lindau, Li-Fraumeni and Turcot syndromes. Recent findings from the GLIOGENE collaboration and the newly identified glioma causing gene POT1, will also be discussed. Genetics. We will describe these disorders from a genetic and clinical standpoint, focusing on the difference in clinical symptoms depending on the underlying gene or germline mutation. Central nervous system (CNS) tumors. Most of these disorders predispose the carriers to a wide range of symptoms. Herein, we will focus particularly on tumors affecting the CNS and discuss improvements of targeted therapy for the particular disorders. PMID:26634384

  20. Fuzzy object models for newborn brain MR image segmentation

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Udupa, Jayaram K.

    2013-03-01

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

  1. MRI and MRS of human brain tumors.

    PubMed

    Hou, Bob L; Hu, Jiani

    2009-01-01

    The purpose of this chapter is to provide an introduction to magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) of human brain tumors, including the primary applications and basic terminology involved. Readers who wish to know more about this broad subject should seek out the referenced books (1. Tofts (2003) Quantitative MRI of the brain. Measuring changes caused by disease. Wiley; Bradley and Stark (1999) 2. Magnetic resonance imaging, 3rd Edition. Mosby Inc; Brown and Semelka (2003) 3. MRI basic principles and applications, 3rd Edition. Wiley-Liss) or reviews (4. Top Magn Reson Imaging 17:127-36, 2006; 5. JMRI 24:709-724, 2006; 6. Am J Neuroradiol 27:1404-1411, 2006).MRI is the most popular means of diagnosing human brain tumors. The inherent difference in the magnetic resonance (MR) properties of water between normal tissues and tumors results in contrast differences on the image that provide the basis for distinguishing tumors from normal tissues. In contrast to MRI, which provides spatial maps or images using water signals of the tissues, proton MRS detects signals of tissue metabolites. MRS can complement MRI because the observed MRS peaks can be linked to inherent differences in biochemical profiles between normal tissues and tumors.The goal of MRI and MRS is to characterize brain tumors, including tumor core, edge, edema, volume, types, and grade. The commonly used brain tumor MRI protocol includes T2-weighted images and T1-weighted images taken both before and after the injection of a contrast agent (typically gadolinium: Gd). The commonly used MRS technique is either point-resolved spectroscopy (PRESS) or stimulated echo acquisition mode (STEAM). PMID:19381963

  2. 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. PMID:21945694

  3. 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.

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

    PubMed

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

    2015-05-01

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

  5. 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

  6. [MR spectroscopy in brain tumors].

    PubMed

    Papanagiotou, P; Backens, M; Grunwald, I Q; Farmakis, G; Politi, M; Roth, C; Reith, W

    2007-06-01

    MRT allows the anatomical visualization of intracerebral space-occupying lesions, and when magnetic resonance spectroscopy (MRS) is used in routine clinical practice it can give more information and be helpful in the diagnosis of such lesions. In MRS with long echo times for nerve tissue there are five metabolites that are particularly significant: N-acetyl aspartate (NAA), creatine, choline, lactate, and lipids. NAA levels are lowered in the presence of intracerebral tumors. Creatine is lowered in situations of hypermetabolic metabolism and elevated in hypometabolic conditions, but remains constant in many pathologic states and can be used as a reliable reference value. With malignant tumors there are usually elevated choline concentrations, reflecting increased membrane synthesis and a higher cell turnover. The lactate level rises following a switch in metabolism from aerobic to anaerobic glycolysis, and this is frequently observed in the presence of malignant tumors. The occurrence of lipid peaks in a tumor spectrum suggests the presence of tissue necroses or metastases. There are typical constellations that are seen on MRS for individual tumors, which are discussed in detail in the present paper. PMID:17530212

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

    NASA Astrophysics Data System (ADS)

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

    2004-01-01

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

  8. 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. PMID:24748242

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

    PubMed Central

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

    2013-01-01

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

  10. 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

  11. 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

  12. [Chemotherapy for brain tumors in adult patients].

    PubMed

    Weller, M

    2008-02-01

    Chemotherapy has become a third major treatment option for patients with brain tumors, in addition to surgery and radiotherapy. The role of chemotherapy in the treatment of gliomas is no longer limited to recurrent disease. Temozolomide has become the standard of care in newly diagnosed glioblastoma. Several ongoing trials seek to define the role of chemotherapy in the primary care of other gliomas. Some of these studies are no longer only based on histological diagnoses, but take into consideration molecular markers such as MGMT promoter methylation and loss of genetic material on chromosomal arms 1p and 19q. Outside such clinical trials chemotherapy is used in addition to radiotherapy, e.g., in anaplastic astrocytoma, medulloblastoma or germ cell tumors, or as an alternative to radiotherapy, e.g., in anaplastic oligodendroglial tumors or low-grade gliomas. In contrast, there is no established role for chemotherapy in other tumors such as ependymomas, meningiomas or neurinomas. Primary cerebral lymphomas are probably the only brain tumors which can be cured by chemotherapy alone and only by chemotherapy. The chemotherapy of brain metastases follows the recommendations for the respective primary tumors. Further, strategies of combined radiochemotherapy using mainly temozolomide or topotecan are currently explored. Leptomeningeal metastases are treated by radiotherapy or systemic or intrathecal chemotherapy depending on their pattern of growth. PMID:18253773

  13. 3D MRI brain image segmentation based on region restricted EM algorithm

    NASA Astrophysics Data System (ADS)

    Li, Zhong; Fan, Jianping

    2008-03-01

    This paper presents a novel algorithm of 3D human brain tissue segmentation and classification in magnetic resonance image (MRI) based on region restricted EM algorithm (RREM). The RREM is a level set segmentation method while the evolution of the contours was driven by the force field composed by the probability density functions of the Gaussian models. Each tissue is modeled by one or more Gaussian models restricted by free shaped contour so that the Gaussian models are adaptive to the local intensities. The RREM is guaranteed to be convergency and achieving the local minimum. The segmentation avoids to be trapped in the local minimum by the split and merge operation. A fuzzy rule based classifier finally groups the regions belonging to the same tissue and forms the segmented 3D image of white matter (WM) and gray matter (GM) which are of major interest in numerous applications. The presented method can be extended to segment brain images with tumor or the images having part of the brain removed with the adjusted classifier.

  14. Segmentation of confocal microscopic image of insect brain

    NASA Astrophysics Data System (ADS)

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

    2002-05-01

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

  15. 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).

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

    PubMed

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

    2004-10-01

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

  17. 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

  18. 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.

  19. Evaluation of automated brain MR image segmentation and volumetry methods.

    PubMed

    Klauschen, Frederick; Goldman, Aaron; Barra, Vincent; Meyer-Lindenberg, Andreas; Lundervold, Arvid

    2009-04-01

    We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies. PMID:18537111

  20. 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.

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

    PubMed

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

    2012-01-16

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

  2. Brain tumors: Special characters for research and banking

    PubMed Central

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

    2015-01-01

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

  3. 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

  4. 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.

  5. Brain segmentation and the generation of cortical surfaces

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  6. 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

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

    PubMed

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

    2015-07-01

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

  9. Automatic segmentation and classification of human brain image based on a fuzzy brain atlas

    NASA Astrophysics Data System (ADS)

    Tan, Ou; Jia, Chunguang; Duan, Huilong; Lu, Weixue

    1998-09-01

    It is difficult to automatically segment and classify tomograph images of actual patient's brain. Therefore, many interactive operations are performed. It is very time consuming and its precision is much depended on the user. In this paper, we combine a brain atlas and 3D fuzzy image segmentation into the image matching. It can not only find out the precise boundary of anatomic structure but also save time of the interactive operation. At first, the anatomic information of atlas is mapped into tomograph images of actual brain with a two step image matching method. Then, based on the mapping result, a 3D fuzzy structure mask is calculated. With the fuzzy information of anatomic structure, a new method of fuzzy clustering based on genetic algorithm is used to segment and classify the real brain image. There is only a minimum requirement of interaction in the whole process, including removing the skull and selecting some intrinsic point pairs.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2007-03-01

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

  12. 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.

  13. Mapping of language brain areas in patients with brain tumors.

    PubMed

    Hyder, Rasha; Kamel, Nidal; Boon, Tang Tong; Reza, Faruque

    2015-08-01

    Language cortex in the human brain shows high variability among normal individuals and may exhibit a considerable shift from its original position due to tumor growth. Mapping the precise location of language areas is important before surgery to avoid postoperative language deficits. In this paper, the Magnetoencephalography (MEG) recording and the MRI scanning of six brain tumorous subjects are used to localize the language specific areas. MEG recordings were performed during two silent reading tasks; silent word reading and silent picture naming. MEG source imaging is performed using distributed source modeling technique called CLARA ("Classical LORETA Analysis Recursively Applied"). Estimated MEG sources are overlaid on individual MRI of each patient to improve interpretation of MEG source imaging results. The results show successful identification of the essential language areas and clear definition of the time course of neural activation connecting them. PMID:26736340

  14. Home care for brain tumor patients

    PubMed Central

    Pace, Andrea; Villani, Veronica; Di Pasquale, Antonella; Benincasa, Dario; Guariglia, Lara; Ieraci, Sonia; Focarelli, Silvia; Carapella, Carmine Maria; Pompili, Alfredo

    2014-01-01

    Background Brain tumor patients are quite different from other populations of cancer patients due to the complexity of supportive care needs, the trajectory of disease, the very short life expectancy, and resulting need for a specific palliative approach. Methods A pilot program of comprehensive palliative care for brain tumor patients was started in the Regina Elena National Cancer Institute of Rome in October 2000, supported by the Lazio Regional Health System. The aim of this model of assistance was to meet patient's needs for care in all stages of disease, support the families, and reduce the rehospitalization rate. The efficacy of the model of care was evaluated analyzing the place of death, caregiver satisfaction, rehospitalization rate, and the impact on costs to the health system. Results From October 2000 to December 2012, 848 patients affected by brain tumor were enrolled in a comprehensive program of neuro-oncological home care. Out of 529 patients who died, 323 (61%) were assisted at home until death, 117 (22.2%) died in hospital, and 89 (16.8%) died in hospice. A cost-effectiveness analysis demonstrated a significant reduction in hospital readmission rates in the last 2 months of life compared with the control group (16.7% vs 38%; P < .001). Conclusions Our findings concerning death at home, rehospitalization rate, quality of life, and satisfaction of patients and their relatives with the care received suggest that a neuro-oncologic palliative home-care program has a positive impact on the quality of care for brain tumor patients, particularly at the end of life. PMID:26034609

  15. 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.

  16. 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

  17. Extracellular Vesicles in Brain Tumor Progression.

    PubMed

    D'Asti, Esterina; Chennakrishnaiah, Shilpa; Lee, Tae Hoon; Rak, Janusz

    2016-04-01

    Brain tumors can be viewed as multicellular 'ecosystems' with increasingly recognized cellular complexity and systemic impact. While the emerging diversity of malignant disease entities affecting brain tissues is often described in reference to their signature alterations within the cellular genome and epigenome, arguably these cell-intrinsic changes can be regarded as hardwired adaptations to a variety of cell-extrinsic microenvironmental circumstances. Conversely, oncogenic events influence the microenvironment through their impact on the cellular secretome, including emission of membranous structures known as extracellular vesicles (EVs). EVs serve as unique carriers of bioactive lipids, secretable and non-secretable proteins, mRNA, non-coding RNA, and DNA and constitute pathway(s) of extracellular exit of molecules into the intercellular space, biofluids, and blood. EVs are also highly heterogeneous as reflected in their nomenclature (exosomes, microvesicles, microparticles) attempting to capture their diverse origin, as well as structural, molecular, and functional properties. While EVs may act as a mechanism of molecular expulsion, their non-random uptake by heterologous cellular recipients defines their unique roles in the intercellular communication, horizontal molecular transfer, and biological activity. In the central nervous system, EVs have been implicated as mediators of homeostasis and repair, while in cancer they may act as regulators of cell growth, clonogenicity, angiogenesis, thrombosis, and reciprocal tumor-stromal interactions. EVs produced by specific brain tumor cell types may contain the corresponding oncogenic drivers, such as epidermal growth factor receptor variant III (EGFRvIII) in glioblastoma (and hence are often referred to as 'oncosomes'). Through this mechanism, mutant oncoproteins and nucleic acids may be transferred horizontally between cellular populations altering their individual and collective phenotypes. Oncogenic pathways

  18. 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

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

    PubMed Central

    Germano, Isabelle; Swiss, Victoria; Casaccia, Patrizia

    2010-01-01

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

  1. 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. PMID:25784642

  2. Adenoviral virotherapy for malignant brain tumors

    PubMed Central

    Nandi, Suvobroto; Lesniak, Maciej S

    2009-01-01

    Glioblastoma multiforme (GBM) is the most common form of primary brain cancer. In the past decade, virotherapy of tumors has gained credence, particularly in glioma management, as these tumors are not completely resectable and tend to micro-metastasize. Adenoviral vectors have an advantage over other viral vectors in that they are relatively non-toxic and do not integrate in the genome. However, the lack of coxsackie and adenovirus receptors (CAR) on surface of gliomas provides for inefficient transduction of wild-type adenoviral vectors in these tumors. By targeting receptors that are over-expressed in gliomas, modified adenoviral constructs have been shown to efficiently infect glioma cells. In addition, by taking advantage of tumor specific promoter (TSP) elements, oncolytic adenoviral vectors offer the promise of selective tumor-specific replication. This dual targeting strategy has enabled specificity in both laboratory and pre-clinical settings. This review looks at current trends in adenoviral virotherapy of gliomas, with an emphasis on targeting modalities and future clinical applications. PMID:19456208

  3. Adenoviral virotherapy for malignant brain tumors.

    PubMed

    Nandi, Suvobroto; Lesniak, Maciej S

    2009-06-01

    Glioblastoma multiforme is the most common form of primary brain cancer. In the past decade, virotherapy of tumors has gained credence, particularly in glioma management, as these tumors are not completely resectable and tend to micro-metastasize. Adenoviral vectors have an advantage over other viral vectors in that they are relatively non-toxic and do not integrate in the genome. However, the lack of coxsackie and adenovirus receptors on surface of gliomas provides for inefficient transduction of wild-type adenoviral vectors in these tumors. By targeting receptors that are overexpressed in gliomas, modified adenoviral constructs have been shown to efficiently infect glioma cells. In addition, by taking advantage of tumor-specific promoter elements, oncolytic adenoviral vectors offer the promise of selective tumor-specific replication. This dual targeting strategy has enabled specificity in both laboratory and pre-clinical settings. This review examines current trends in adenoviral virotherapy of gliomas, with an emphasis on targeting modalities and future clinical applications. PMID:19456208

  4. Canine spontaneous brain tumors: A large animal model for BNCT

    SciTech Connect

    Gavin, P.R.; Kraft, S.L.; Wendling, L.R.; Miller, D.L.

    1988-01-01

    Brain tumors occur spontaneously on dogs with an incidence similar to that in humans. Brain tumors of dogs have histologic, radiologic, and other diagnostic similarities to human brain tumors. Tumor kinetics and biologic behavior of these tumors in dogs are also similar to that in man. Recent studies indicate that conventional radiation therapy of brain tumors of dogs result in a survival interval appropriate to study the late radiation reactions in the surrounding normal brain and other tissues within the irradiated field. The relatively large size of the dog allows identical diagnostic and therapeutic modalities and methodology. The dog's head size enables the complex dosimetric variables to be relevant to that found in human radiation therapy. For these reasons, spontaneous brain tumors in the dog are an excellent model to study neuon capture theory (NCT). 7 refs., 1 fig., 3 tabs.

  5. An overview of therapeutic approaches to brain tumor stem cells

    PubMed Central

    2012-01-01

    Primary and secondary malignant central nervous system (CNS) tumors are devastating invasive tumors able to give rise to many kinds of differentiated tumor cells. Glioblastoma multiform (GBM), is the most malignant brain tumor, in which its growth and persistence depend on cancer stem cells with enhanced DNA damage repair program that also induces recurrence and resists current chemo- and radiotherapies. Unlike non-tumor stem cells, tumor stem cells lack the normal mechanisms that regulate proliferation and differentiation, resulting in uncontrolled production and incomplete differentiation of tumor cells. In current paper recent developments and new researches in the field of brain tumor stem cells have been reviewed. PMID:23483074

  6. 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

  7. [Head and Neck Tumor Segmentation Based on Augmented Gradient Level Set Method].

    PubMed

    Zhang, Qiongmin; Zhang, Jing; Wang, Mintang; He, Ling; Men, Yi; Wei, Jun; Haung, Hua

    2015-08-01

    To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis. PMID:26710464

  8. Photodynamic Therapy for Malignant Brain Tumors.

    PubMed

    Akimoto, Jiro

    2016-04-15

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

  9. Photodynamic Therapy for Malignant Brain Tumors

    PubMed Central

    AKIMOTO, Jiro

    2016-01-01

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

  10. Molecular Culprits Generating Brain Tumor Stem Cells

    PubMed Central

    Oh, Se-Yeong

    2013-01-01

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

  11. Multifocal brain radionecrosis masquerading as tumor dissemination

    SciTech Connect

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

    1984-01-01

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

  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. Intraoperative MRI in pediatric brain tumors.

    PubMed

    Choudhri, Asim F; Siddiqui, Adeel; Klimo, Paul; Boop, Frederick A

    2015-09-01

    Intraoperative magnetic resonance imaging (iMRI) has emerged as an important tool in guiding the surgical management of children with brain tumors. Recent advances have allowed utilization of high field strength systems, including 3-tesla MRI, resulting in diagnostic-quality scans that can be performed while the child is on the operating table. By providing information about the possible presence of residual tumor, it allows the neurosurgeon to both identify and resect any remaining tumor that is thought to be safely accessible. By fusing the newly obtained images with the surgical guidance software, the images have the added value of aiding in navigation to any residual tumor. This is important because parenchyma often shifts during surgery. It also gives the neurosurgeon insight into whether any immediate postoperative complications have occurred. If any complications have occurred, the child is already in the operating room and precious minutes lost in transport and communications are saved. In this article we review the three main approaches to an iMRI system design. We discuss the possible roles for iMRI during intraoperative planning and provide guidance to help radiologists and neurosurgeons alike in the collaborative management of these children. PMID:26346145

  14. Multifunctional nanoparticles for brain tumor imaging and therapy.

    PubMed

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

    2014-02-01

    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. 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

  16. Involvement of tumor acidification in brain cancer pathophysiology

    PubMed Central

    Honasoge, Avinash; Sontheimer, Harald

    2013-01-01

    Gliomas, primary brain cancers, are characterized by remarkable invasiveness and fast growth. While they share many qualities with other solid tumors, gliomas have developed special mechanisms to convert the cramped brain space and other limitations afforded by the privileged central nervous system into pathophysiological advantages. In this review we discuss gliomas and other primary brain cancers in the context of acid-base regulation and interstitial acidification; namely, how the altered proton (H+) content surrounding these brain tumors influences tumor development in both autocrine and paracrine manners. As proton movement is directly coupled to movement of other ions, pH serves as both a regulator of cell activity as well as an indirect readout of other cellular functions. In the case of brain tumors, these processes result in pathophysiology unique to the central nervous system. We will highlight what is known about pH-sensitive processes in brain tumors in addition to gleaning insight from other solid tumors. PMID:24198789

  17. CAUSAL MARKOV RANDOM FIELD FOR BRAIN MR IMAGE SEGMENTATION

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  19. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  2. Chemotherapy for malignant brain tumors of childhood

    PubMed Central

    Gottardo, Nicholas G.; Gajjar, Amar

    2009-01-01

    During the past 3 decades, chemotherapeutic agents have been extensively evaluated for the treatment of pediatric brain tumors in a myriad of schedules, doses, and combinations. Remarkable advances in outcome have been achieved for certain groups of children, notably those with medulloblastoma, and chemotherapy has played a key role. However, improvements in survival are obtained at a high cost to quality of life. In addition, the success achieved for medulloblastoma is offset by a lack of progress for high-grade glioma. Despite decades of intensive investigation, no single chemotherapeutic regimen stands out as particularly beneficial for children with high-grade glioma, with the vast majority of these patients succumbing to their disease. A plateau in efficacy has been reached. Further treatment intensification using conventional nonspecific chemotherapy is more likely to result in additional toxicity without major advances in survival. Genomewide analysis using microarray technology has contributed significantly to our understanding of tumor biology. This knowledge has shifted the focus onto novel agents that target molecular changes crucial for tumor proliferation or survival. These selective agents are likely to be less toxic to normal cells and it is anticipated they will be more effective than the nonspecific chemotherapeutic agents currently used. PMID:18952581

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

    PubMed

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

    2013-01-01

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

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

    SciTech Connect

    Not Available

    1986-09-01

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

  5. A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors.

    PubMed

    Reddick, W E; Mulhern, R K; Elkin, T D; Glass, J O; Merchant, T E; Langston, J W

    1998-05-01

    In the treatment of children with brain tumors, balancing the efficacy of treatment against commonly observed side effects is difficult because of a lack of quantitative measures of brain damage that can be correlated with the intensity of treatment. We quantitatively assessed volumes of brain parenchyma on magnetic resonance (MR) images using a hybrid combination of the Kohonen self-organizing map for segmentation and a multilayer backpropagation neural network for tissue classification. Initially, we analyzed the relationship between volumetric differences and radiologists' grading of atrophy in 80 subjects. This investigation revealed that brain parenchyma and white matter volumes significantly decreased as atrophy increased, whereas gray matter volumes had no relationship with atrophy. Next, we compared 37 medulloblastoma patients treated with surgery, irradiation, and chemotherapy to 19 patients treated with surgery and irradiation alone. This study demonstrated that, in these patients, chemotherapy had no significant effect on brain parenchyma, white matter, or gray matter volumes. We then investigated volumetric differences due to cranial irradiation in 15 medulloblastoma patients treated with surgery and radiation therapy, and compared these with a group of 15 age-matched patients with low-grade astrocytoma treated with surgery alone. With a minimum follow-up of one year after irradiation, all radiation-treated patients demonstrated significantly reduced white matter volumes, whereas gray matter volumes were relatively unchanged compared with those of age-matched patients treated with surgery alone. These results indicate that reductions in cerebral white matter: 1) are correlated significantly with atrophy; 2) are not related to chemotherapy; and 3) are correlated significantly with irradiation. This hybrid neural network analysis of subtle brain volume differences with magnetic resonance may constitute a direct measure of treatment-induced brain damage

  6. Tumor segmentation of multi-echo MR T2-weighted images with morphological operators

    NASA Astrophysics Data System (ADS)

    Torres, W.; Martín-Landrove, M.; Paluszny, M.; Figueroa, G.; Padilla, G.

    2009-02-01

    In the present work an automatic brain tumor segmentation procedure based on mathematical morphology is proposed. The approach considers sequences of eight multi-echo MR T2-weighted images. The relaxation time T2 characterizes the relaxation of water protons in the brain tissue: white matter, gray matter, cerebrospinal fluid (CSF) or pathological tissue. Image data is initially regularized by the application of a log-convex filter in order to adjust its geometrical properties to those of noiseless data, which exhibits monotonously decreasing convex behavior. Finally the regularized data is analyzed by means of an 8-dimensional morphological eccentricity filter. In a first stage, the filter was used for the spatial homogenization of the tissues in the image, replacing each pixel by the most representative pixel within its structuring element, i.e. the one which exhibits the minimum total distance to all members in the structuring element. On the filtered images, the relaxation time T2 is estimated by means of least square regression algorithm and the histogram of T2 is determined. The T2 histogram was partitioned using the watershed morphological operator; relaxation time classes were established and used for tissue classification and segmentation of the image. The method was validated on 15 sets of MRI data with excellent results.

  7. 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.

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

    PubMed

    Rossmeisl, John H

    2014-11-01

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

  9. Yoga Therapy in Treating Patients With Malignant Brain Tumors

    ClinicalTrials.gov

    2015-07-27

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

  10. [MRI with dynamic contrast enhancement in brain tumors].

    PubMed

    Panfilenko, A F; Iakovlev, S A; Pozdniakov, A V; Tiumin, L A; Shcherbuk, A Iu

    2013-01-01

    Magnetic resonance imaging (MRI) is the leading method of radiation diagnosis of brain tumors. In conditions of the artificial contrast enhancement there are more clearly differentiated the boundaries of the tumor node on the back of peritumorous edema and identified structural features of the tumor. The purpose of this study was to examine indicators of the dynamics of accumulation and removal of contrast agents by brain tumors in MRI technique with dynamic contrast and identify opportunities of this method in the differential diagnosis of various types of tumors. PMID:23814831

  11. 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.

  12. 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.

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

    PubMed

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

    2007-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel

    2014-03-01

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

  15. 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

  16. 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. PMID:24998288

  17. 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

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

    PubMed

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

    2004-08-01

    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

  19. 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. PMID:27180580

  20. 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

  1. Application of artificial neural network in simulating subjective evaluation of tumor segmentation

    NASA Astrophysics Data System (ADS)

    Lv, Dongjiao; Deng, Xiang

    2011-03-01

    Systematic validation of tumor segmentation technique is very important in ensuring the accuracy and reproducibility of tumor segmentation algorithm in clinical applications. In this paper, we present a new method for evaluating 3D tumor segmentation using Artificial Neural Network (ANN) and combined objective metrics. In our evaluation method, a three-layer feed-forwarding backpropagation ANN is first trained to simulate radiologist's subjective rating using a set of objective metrics. The trained neural network is then used to evaluate the tumor segmentation on a five-point scale in a way similar to expert's evaluation. The accuracy of segmentation evaluation is quantified using average correct rank and frequency of the reference rating in the top ranks of simulated score list. Experimental results from 93 lesions showed that our evaluation method performs better than individual metrics. The optimal combination of metrics from normalized volume difference, volume overlap, Root Mean Square symmetric surface distance and maximum symmetric surface distance showed the smallest average correct rank (1.43) and highest frequency of the reference rating in the top two places of simulated rating list (93.55%). Our results also demonstrate that the ANN based non-linear combination method showed better evaluation accuracy than linear combination method in all performance measures. Our evaluation technique has the potential to facilitate large scale segmentation validation study by predicting radiologists rating, and to assist development of new tumor segmentation algorithms. It can also be extended to validation of segmentation algorithms for other applications.

  2. Thymus-derived rather than tumor-induced regulatory T cells predominate in brain tumors

    PubMed Central

    Wainwright, Derek A.; Sengupta, Sadhak; Han, Yu; Lesniak, Maciej S.

    2011-01-01

    Glioblastoma multiforme (GBM) is a highly malignant brain tumor with an average survival time of 15 months. Previously, we and others demonstrated that CD4+FoxP3+ regulatory T cells (Tregs) infiltrate human GBM as well as mouse models that recapitulate malignant brain tumors. However, whether brain tumor-resident Tregs are thymus-derived natural Tregs (nTregs) or induced Tregs (iTregs), by the conversion of conventional CD4+ T cells, has not been established. To investigate this question, we utilized the i.c. implanted GL261 cell-based orthotopic mouse model, the RasB8 transgenic astrocytoma mouse model, and a human GBM tissue microarray. We demonstrate that Tregs in brain tumors are predominantly thymus derived, since thymectomy, prior to i.c. GL261 cell implantation, significantly decreased the level of Tregs in mice with brain tumors. Accordingly, most Tregs in human GBM and mouse brain tumors expressed the nTreg transcription factor, Helios. Interestingly, a significant effect of the brain tumor microenvironment on Treg lineage programming was observed, based on higher levels of brain tumor-resident Tregs expressing glucocorticoid-induced tumor necrosis factor receptor and CD103 and lower levels of Tregs expressing CD62L and CD45RB compared with peripheral Tregs. Furthermore, there was a higher level of nTregs in brain tumors that expressed the proliferative marker Ki67 compared with iTregs and conventional CD4+ T cells. Our study demonstrates that future Treg-depleting therapies should aim to selectively target systemic rather than intratumoral nTregs in brain tumor-specific immunotherapeutic strategies. PMID:21908444

  3. Thymus-derived rather than tumor-induced regulatory T cells predominate in brain tumors.

    PubMed

    Wainwright, Derek A; Sengupta, Sadhak; Han, Yu; Lesniak, Maciej S

    2011-12-01

    Glioblastoma multiforme (GBM) is a highly malignant brain tumor with an average survival time of 15 months. Previously, we and others demonstrated that CD4(+)FoxP3(+) regulatory T cells (Tregs) infiltrate human GBM as well as mouse models that recapitulate malignant brain tumors. However, whether brain tumor-resident Tregs are thymus-derived natural Tregs (nTregs) or induced Tregs (iTregs), by the conversion of conventional CD4(+) T cells, has not been established. To investigate this question, we utilized the i.c. implanted GL261 cell-based orthotopic mouse model, the RasB8 transgenic astrocytoma mouse model, and a human GBM tissue microarray. We demonstrate that Tregs in brain tumors are predominantly thymus derived, since thymectomy, prior to i.c. GL261 cell implantation, significantly decreased the level of Tregs in mice with brain tumors. Accordingly, most Tregs in human GBM and mouse brain tumors expressed the nTreg transcription factor, Helios. Interestingly, a significant effect of the brain tumor microenvironment on Treg lineage programming was observed, based on higher levels of brain tumor-resident Tregs expressing glucocorticoid-induced tumor necrosis factor receptor and CD103 and lower levels of Tregs expressing CD62L and CD45RB compared with peripheral Tregs. Furthermore, there was a higher level of nTregs in brain tumors that expressed the proliferative marker Ki67 compared with iTregs and conventional CD4(+) T cells. Our study demonstrates that future Treg-depleting therapies should aim to selectively target systemic rather than intratumoral nTregs in brain tumor-specific immunotherapeutic strategies. PMID:21908444

  4. 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

  5. Multidisciplinary pediatric brain tumor clinics: the key to successful treatment?

    PubMed

    Abdel-Baki, Mohamed S; Hanzlik, Emily; Kieran, Mark W

    2015-01-01

    Tumors of the CNS are the most common solid tumors diagnosed in childhood. As technology and research in cancer care are advancing, more specialties are involved in the diagnosis, treatment and follow-up of children with brain tumors. Multidisciplinary clinics have become the standard of care for cancer care throughout the USA, and specialty clinics focused on particular cancer types are gaining attention in improving the patient outcomes and satisfaction. We will discuss the role of multidisciplinary clinics, in an attempt to create preliminary guidelines on establishing and maintaining a multidisciplinary brain tumor clinic in order to optimize the care of the patients and their families. PMID:25923018

  6. Dendrimer-mediated approaches for the treatment of brain tumor.

    PubMed

    Dwivedi, Nitin; Shah, Jigna; Mishra, Vijay; Mohd Amin, Mohd Cairul Iqbal; Iyer, Arun K; Tekade, Rakesh Kumar; Kesharwani, Prashant

    2016-05-01

    Worldwide, the cancer appeared as one of the most leading cause of morbidity and mortality. Among the various cancer types, brain tumors are most life threatening with low survival rate. Every year approximately 238,000 new cases of brain and other central nervous system tumors are diagnosed. The dendrimeric approaches have a huge potential for diagnosis and treatment of brain tumor with targeting abilities of molecular cargoes to the tumor sites and the efficiency of crossing the blood brain barrier and penetration to brain after systemic administration. The various generations of dendrimers have been designed as novel targeted drug delivery tools for new therapies including sustained drug release, gene therapy, and antiangiogenic activities. At present era, various types of dendrimers like PAMAM, PPI, and PLL dendrimers validated them as milestones for the treatment and diagnosis of brain tumor as well as other cancers. This review highlights the recent research, opportunities, advantages, and challenges involved in development of novel dendrimeric complex for the therapy of brain tumor. PMID:26928261

  7. 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. PMID:25225894

  8. 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.

  9. Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach.

    PubMed

    Foo, Jung Leng; Miyano, Go; Lobe, Thom; Winer, Eliot

    2011-01-01

    Automatic segmentation of tumors is a complicated and difficult process as most tumors are rarely clearly delineated from healthy tissues. A new method for probabilistic segmentation to efficiently segment tumors within CT data and to improve the use of digital medical data in diagnosis has been developed. Image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels' spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices. The seed selection algorithm performs a greedy search by searching for pixels with matching intensity close to the location of the original seed point. A total of ten CT datasets were used as test cases, each with varying difficulty in terms of automatic segmentation. Five test cases had mean false positive error rates less than 10%, and four test cases had mean false negative error rates less than 10% when compared to manual segmentation of those tumors by radiologists. PMID:21146165

  10. 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.

  11. 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.

  12. 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.

  13. Cortical Plasticity in the Setting of Brain Tumors.

    PubMed

    Fisicaro, Ryan A; Jost, Ethan; Shaw, Katharina; Brennan, Nicole Petrovich; Peck, Kyung K; Holodny, Andrei I

    2016-02-01

    Cortical reorganization of function due to the growth of an adjacent brain tumor has clearly been demonstrated in a number of surgically proven cases. Such cases demonstrate the unmistakable implications for the neurosurgical treatment of brain tumors, as the cortical function may not reside where one may initially suspect based solely on the anatomical magnetic resonance imaging (MRI). Consequently, preoperative localization of eloquent areas adjacent to a brain tumor is necessary, as this may demonstrate unexpected organization, which may affect the neurosurgical approach to the lesion. However, in interpreting functional MRI studies, the interpreting physician must be cognizant of artifacts, which may limit the accuracy of functional MRI in the setting of brain tumors. PMID:26848558

  14. Childhood Brain and Spinal Cord Tumors Treatment Overview

    MedlinePlus

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

  15. Treatment of Newly Diagnosed and Recurrent Childhood Brain Tumors

    MedlinePlus

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

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

    PubMed Central

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

    2016-01-01

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

  17. Brain and Spinal Tumors: Hope through Research

    MedlinePlus

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

  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. Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.

    PubMed

    Zhou, Yongxin; Bai, Jing

    2007-01-01

    A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction. PMID:17260863

  20. The impact of dietary isoflavonoids on malignant brain tumors.

    PubMed

    Sehm, Tina; Fan, Zheng; Weiss, Ruth; Schwarz, Marc; Engelhorn, Tobias; Hore, Nirjhar; Doerfler, Arnd; Buchfelder, Michael; Eyüpoglu, Iiker Y; Savaskan, Nic E

    2014-08-01

    Poor prognosis and limited therapeutic options render malignant brain tumors one of the most devastating diseases in clinical medicine. Current treatment strategies attempt to expand the therapeutic repertoire through the use of multimodal treatment regimens. It is here that dietary fibers have been recently recognized as a supportive natural therapy in augmenting the body's response to tumor growth. Here, we investigated the impact of isoflavonoids on primary brain tumor cells. First, we treated glioma cell lines and primary astrocytes with various isoflavonoids and phytoestrogens. Cell viability in a dose-dependent manner was measured for biochanin A (BCA), genistein (GST), and secoisolariciresinol diglucoside (SDG). Dose-response action for the different isoflavonoids showed that BCA is highly effective on glioma cells and nontoxic for normal differentiated brain tissues. We further investigated BCA in ex vivo and in vivo experimentations. Organotypic brain slice cultures were performed and treated with BCA. For in vivo experiments, BCA was intraperitoneal injected in tumor-implanted Fisher rats. Tumor size and edema were measured and quantified by magnetic resonance imaging (MRI) scans. In vascular organotypic glioma brain slice cultures (VOGIM) we found that BCA operates antiangiogenic and neuroprotective. In vivo MRI scans demonstrated that administered BCA as a monotherapy was effective in reducing significantly tumor-induced brain edema and showed a trend for prolonged survival. Our results revealed that dietary isoflavonoids, in particular BCA, execute toxicity toward glioma cells, antiangiogenic, and coevally neuroprotective properties, and therefore augment the range of state-of-the-art multimodal treatment approach. PMID:24898306

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

    PubMed Central

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

    2014-01-01

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

  2. 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

  3. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

    PubMed Central

    Chen, Wenan; Smith, Rebecca; Ji, Soo-Yeon; Ward, Kevin R; Najarian, Kayvan

    2009-01-01

    Background Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information. Methods First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases. Results Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images. Conclusion The experiments show the reliability of the proposed algorithms. The novelty of the proposed

  4. 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.

  5. 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

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

    PubMed

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

    2016-07-01

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

  7. 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

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

    PubMed

    Karathanasis, Efstathios; Ghaghada, Ketan B

    2016-09-01

    Despite advancements in surgery and radiotherapy, the aggressive forms of brain tumors, such as gliomas, are still uniformly lethal with current therapies offering only palliation complicated by significant toxicities. Gliomas are characteristically diffuse with infiltrating edges, resistant to drugs and nearly inaccessible to systemic therapies due to the brain-tumor barrier. Currently, aggressive efforts are underway to further understand brain-tumor's microenvironment and identify brain tumor cell-specific regulators amenable to pharmacologic interventions. While new potent agents are continuously becoming available, efficient drug delivery to brain tumors remains a limiting factor. To tackle the drug delivery issues, a multicomponent chain-like nanoparticle has been developed. These nanochains are comprised of iron oxide nanospheres and a drug-loaded liposome chemically linked into a 100-nm linear, chain-like assembly with high precision. The nanochain possesses a unique ability to scavenge the tumor endothelium. By utilizing effective vascular targeting, the nanochains achieve rapid deposition on the vascular bed of glioma sites establishing well-distributed drug reservoirs on the endothelium of brain tumors. After reaching the target sites, an on-command, external low-power radiofrequency field can remotely trigger rapid drug release, due to mechanical disruption of the liposome, facilitating widespread and effective drug delivery into regions harboring brain tumor cells. Integration of the nanochain delivery system with the appropriate combination of complementary drugs has the potential to unfold the field and allow significant expansion of therapies for the disease where success is currently very limited. WIREs Nanomed Nanobiotechnol 2016, 8:678-695. doi: 10.1002/wnan.1387 For further resources related to this article, please visit the WIREs website. PMID:26749497

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    SciTech Connect

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

    2014-11-01

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

  11. 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

  12. Evolution of Brain Tumor and Stability of Geometric Invariants

    PubMed Central

    Tawbe, K.; Cotton, F.; Vuillon, L.

    2008-01-01

    This paper presents a method to reconstruct and to calculate geometric invariants on brain tumors. The geometric invariants considered in the paper are the volume, the area, the discrete Gauss curvature, and the discrete mean curvature. The volume of a tumor is an important aspect that helps doctors to make a medical diagnosis. And as doctors seek a stable calculation, we propose to prove the stability of some invariants. Finally, we study the evolution of brain tumor as a function of time in two or three years depending on patients with MR images every three or six months. PMID:19325922

  13. 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

  14. PDE5 Inhibitors Enhance Tumor Permeability and Efficacy of Chemotherapy in a Rat Brain Tumor Model

    PubMed Central

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

    2008-01-01

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

  15. Gold nanoparticle imaging and radiotherapy of brain tumors in mice

    PubMed Central

    Hainfeld, James F; Smilowitz, Henry M; O'Connor, Michael J; Dilmanian, Farrokh Avraham; Slatkin, Daniel N

    2013-01-01

    Aim To test intravenously injected gold nanoparticles for x-ray imaging and radiotherapy enhancement of large, imminently lethal, intracerebral malignant gliomas. Materials & methods Gold nanoparticles approximately 11 nm in size were injected intravenously and brains imaged using microcomputed tomography. A total of 15 h after an intravenous dose of 4 g Au/kg was administered, brains were irradiated with 30 Gy 100 kVp x-rays. Results Gold uptake gave a 19:1 tumor-to-normal brain ratio with 1.5% w/w gold in tumor, calculated to increase local radiation dose by approximately 300%. Mice receiving gold and radiation (30 Gy) demonstrated 50% long term (>1 year) tumor-free survival, whereas all mice receiving radiation only died. Conclusion Intravenously injected gold nanoparticles cross the blood–tumor barrier, but are largely blocked by the normal blood–brain barrier, enabling high-resolution computed tomography tumor imaging. Gold radiation enhancement significantly improved long-term survival compared with radiotherapy alone. This approach holds promise to improve therapy of human brain tumors and other cancers. PMID:23265347

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

    SciTech Connect

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

    1991-04-01

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

  17. 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.

  18. 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

  19. 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. PMID:26276018

  20. A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine.

    PubMed

    Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

    2013-10-01

    In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

  1. 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

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed

    Schittenhelm, Jens; Tabatabai, Ghazaleh; Sipos, Bence

    2016-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  5. 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.

  6. Regulatory T cells actively infiltrate metastatic brain tumors.

    PubMed

    Sugihara, Adam Quasar; Rolle, Cleo E; Lesniak, Maciej S

    2009-06-01

    Regulatory T cells (CD4+CD25+FoxP3+, Treg) have been shown to play a major role in suppression of the immune response to malignant gliomas. In this study, we investigated the kinetics of Treg infiltration in metastatic brain tumor models, including melanoma, breast and colon cancers. Our data indicate that both CD4+ and Treg infiltration are significantly increased throughout the time of metastatic tumor progression. These findings were recapitulated in human CNS tumor samples of metastatic melanoma and non-small cell lung carcinoma. Collectively, these data support investigating immunotherapeutic strategies targeting Treg in metastatic CNS tumors. PMID:19424570

  7. Automated lung tumor segmentation for whole body PET volume based on novel downhill region growing

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

    We propose an automated lung tumor segmentation method for whole body PET images based on a novel downhill region growing (DRG) technique, which regards homogeneous tumor hotspots as 3D monotonically decreasing functions. The method has three major steps: thoracic slice extraction with K-means clustering of the slice features; hotspot segmentation with DRG; and decision tree analysis based hotspot classification. To overcome the common problem of leakage into adjacent hotspots in automated lung tumor segmentation, DRG employs the tumors' SUV monotonicity features. DRG also uses gradient magnitude of tumors' SUV to improve tumor boundary definition. We used 14 PET volumes from patients with primary NSCLC for validation. The thoracic region extraction step achieved good and consistent results for all patients despite marked differences in size and shape of the lungs and the presence of large tumors. The DRG technique was able to avoid the problem of leakage into adjacent hotspots and produced a volumetric overlap fraction of 0.61 +/- 0.13 which outperformed four other methods where the overlap fraction varied from 0.40 +/- 0.24 to 0.59 +/- 0.14. Of the 18 tumors in 14 NSCLC studies, 15 lesions were classified correctly, 2 were false negative and 15 were false positive.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  9. Cognitive Screening in Brain Tumors: Short but Sensitive Enough?

    PubMed Central

    Robinson, Gail A.; Biggs, Vivien; Walker, David G.

    2015-01-01

    Cognitive deficits in brain tumors are generally thought to be relatively mild and non-specific, although recent evidence challenges this notion. One possibility is that cognitive screening tools are being used to assess cognitive functions but their sensitivity to detect cognitive impairment may be limited. For improved sensitivity to recognize mild and/or focal cognitive deficits in brain tumors, neuropsychological evaluation tailored to detect specific impairments has been thought crucial. This study investigates the sensitivity of a cognitive screening tool, the Montreal Cognitive Assessment (MoCA), compared to a brief but tailored cognitive assessment (CA) for identifying cognitive deficits in an unselected primary brain tumor sample (i.e., low/high-grade gliomas, meningiomas). Performance is compared on broad measures of impairment: (a) number of patients impaired on the global screening measure or in any cognitive domain; and (b) number of cognitive domains impaired and specific analyses of MoCA-Intact and MoCA-Impaired patients on specific cognitive tests. The MoCA-Impaired group obtained lower naming and word fluency scores than the MoCA-Intact group, but otherwise performed comparably on cognitive tests. Overall, based on our results from patients with brain tumor, the MoCA has extremely poor sensitivity for detecting cognitive impairments and a brief but tailored CA is necessary. These findings will be discussed in relation to broader issues for clinical management and planning, as well as specific considerations for neuropsychological assessment of brain tumor patients. PMID:25815273

  10. Convection-enhanced delivery for the treatment of brain tumors

    PubMed Central

    Debinski, Waldemar; Tatter, Stephen B

    2013-01-01

    The brain is highly accessible for nutrients and oxygen, however delivery of drugs to malignant brain tumors is a very challenging task. Convection-enhanced delivery (CED) has been designed to overcome some of the difficulties so that pharmacological agents that would not normally cross the BBB can be used for treatment. Drugs are delivered through one to several catheters placed stereotactically directly within the tumor mass or around the tumor or the resection cavity. Several classes of drugs are amenable to this technology including standard chemotherapeutics or novel experimental targeted drugs. The first Phase III trial for CED-delivered, molecularly targeted cytotoxin in the treatment of recurrent glioblastoma multiforme has been accomplished and demonstrated objective clinical efficacy. The lessons learned from more than a decade of attempts at exploiting CED for brain cancer treatment weigh critically for its future clinical applications. The main issues center around the type of catheters used, number of catheters and their exact placement; pharmacological formulation of drugs, prescreening patients undergoing treatment and monitoring the distribution of drugs in tumors and the tumor-infiltrated brain. It is expected that optimizing CED will make this technology a permanent addition to clinical management of brain malignancies. PMID:19831841