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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

    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

  18. 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.

  19. 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

  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. 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 ...

  10. 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 ...

  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. 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 ...

  3. 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 ...

  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. 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.

  8. 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

  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

    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

  2. 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

  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 Central

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

    2015-01-01

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

  13. 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

  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. 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

  2. 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

  3. 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

  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. 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

  1. 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

  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 Diagnosis and Therapy

    PubMed Central

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

    2013-01-01

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

  15. 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

  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. 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

  13. 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.

  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. 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

  12. 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

  13. 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

  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

  11. Patented nanomedicines for the treatment of brain tumors.

    PubMed

    Caruso, Gerardo; Raudino, Giuseppe; Caffo, Maria

    2013-11-01

    Patients affected by malignant brain tumors present an extremely poor prognosis, notwithstanding improvements in surgery techniques and therapeutic protocols. Brain tumor treatment has been principally hampered by limited drug delivery across the blood-brain barrier (BBB). An efficacious chemotherapeutic treatment requires a pharmacological agent that can penetrate the BBB and target neoplastic cells. Nanotechnology involves the design, synthesis and characterization of materials that have a functional organization in at least one dimension on the nanometer scale. Nanoparticle systems can represent optimal devices for delivery of various drugs into the brain across the BBB. Nanoparticle drug-delivery systems can also be used to provide targeted delivery of drugs, improve bioavailability and sustain release of drugs for systemic delivery. In this patent review, the recent studies of certain nanoparticle systems in treatment of brain tumors are summarized. Common nanoparticles systems include polymeric nanoparticles, lipid nanoparticles and inorganic nanoparticles. Various patents of nanoparticle systems able to across the BBB to target brain tumors are also reported and discussed. PMID:24237240

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

    NASA Astrophysics Data System (ADS)

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

    2011-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    SciTech Connect

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

    2015-04-24

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

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

    NASA Astrophysics Data System (ADS)

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

    1998-11-01

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

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

    PubMed

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

    2009-01-01

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

  17. Culture and Isolation of Brain Tumor Initiating Cells.

    PubMed

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

    2015-01-01

    Brain tumors are typically composed of heterogeneous cells that exhibit distinct phenotypic characteristics and proliferative potentials. Only a relatively small fraction of cells in the tumor with stem cell properties, termed brain tumor initiating cells (BTICs), possess an ability to differentiate along multiple lineages, self-renew, and initiate tumors in vivo. This unit describes protocols for the culture and isolation BTICs. We applied culture conditions and assays originally used for normal neural stem cells (NSCs) in vitro to a variety of brain tumors. Using fluorescence-activated cell sorting for the neural precursor cell surface marker CD133/CD15, BTICs can be isolated and studied prospectively. Isolation of BTICs from GBM bulk tumor will enable examination of dissimilar morphologies, self-renewal capacities, tumorigenicity, and therapeutic sensitivities. As cancer is also considered a disease of unregulated self-renewal and differentiation, an understanding of BTICs is fundamental to understanding tumor growth. Ultimately, it will lead to novel drug discovery approaches that strategically target the functionally relevant BTIC population. PMID:26237571

  18. New strategies to deliver anticancer drugs to brain tumors

    PubMed Central

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

    2009-01-01

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

  19. Diphtheria toxin-based targeted toxin therapy for brain tumors.

    PubMed

    Li, Yan Michael; Vallera, Daniel A; Hall, Walter A

    2013-09-01

    Targeted toxins (TT) are molecules that bind cell surface antigens or receptors such as the transferrin or interleukin-13 receptor that are overexpressed in cancer. After internalization, the toxin component kills the cell. These recombinant proteins consist of an antibody or carrier ligand coupled to a modified plant or bacterial toxin such as diphtheria toxin (DT). These fusion proteins are very effective against brain cancer cells that are resistant to radiation therapy and chemotherapy. TT have shown an acceptable profile for toxicity and safety in animal studies and early clinical trials have demonstrated a therapeutic response. This review summarizes the characteristics of DT-based TT, the animal studies in malignant brain tumors and early clinical trial results. Obstacles to the successful treatment of brain tumors include poor penetration into tumor, the immune response to DT and cancer heterogeneity. PMID:23695514

  20. Neurospecific proteins in the serum of patients with brain tumors.

    PubMed

    Lyubimova, N V; Toms, M G; Popova, E E; Bondarenko, Y V; Krat, V B; Kushlinskii, N E

    2011-04-01

    Neurospecific proteins S-100 and GFAP were measured in the serum of 145 patients with neural tumors and 69 healthy individuals. In patients with glyoblastomas, the concentrations of S-100 and GFAP were significantly higher than in patients with anaplastic astrocytomas, benign meningiomas, and brain metastases and in healthy individuals. Serum S-100 concentrations in patients with anaplastic astrocytomas, benign meningiomas, and brain metastases were similar; significant difference from the control was found only for patients with cerebral metastases. A specific feature of GFAP was high incidence of its detection in patients with glioblastomas (83%) compared to other groups of patients with neural tumors and healthy volunteers who demonstrated practically zero level of this protein. These findings attest to the possibility of using S-100 as an additional biochemical criterion of brain involvement in tumor patients and GFAP as a glioblastoma marker. PMID:22235430

  1. A noninvasive multimodal technique to monitor brain tumor vascularization

    NASA Astrophysics Data System (ADS)

    Saxena, Vishal; Gonzalez-Gomez, Ignacio; Laug, Walter E.

    2007-09-01

    Determination of tumor oxygenation at the microvascular level will provide important insight into tumor growth, angiogenesis, necrosis and therapeutic response and will facilitate to develop protocols for studying tumor behavior. The non-ionizing near infrared spectroscopy (NIRS) technique has the potential to differentiate lesion and hemoglobin dynamics; however, it has a limited spatial resolution. On the other hand, magnetic resonance imaging (MRI) has achieved high spatial resolution with excellent tissue discrimination but is more susceptible to limited ability to monitor the hemoglobin dynamics. In the present work, the vascular status and the pathophysiological changes that occur during tumor vascularization are studied in an orthotopic brain tumor model. A noninvasive multimodal approach based on the NIRS technique, namely steady state diffuse optical spectroscopy (SSDOS) along with MRI, is applied for monitoring the concentrations of oxyhemoglobin, deoxyhemoglobin and water within tumor region. The concentrations of oxyhemoglobin, deoxyhemoglobin and water within tumor vasculature are extracted at 15 discrete wavelengths in a spectral window of 675-780 nm. We found a direct correlation between tumor size, intratumoral microvessel density and tumor oxygenation. The relative decrease in tumor oxygenation with growth indicates that though blood vessels infiltrate and proliferate the tumor region, a hypoxic trend is clearly present.

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

    NASA Astrophysics Data System (ADS)

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

    2001-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed

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

    2006-02-01

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

  5. PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration

    PubMed Central

    Niethammer, Marc; Akbari, Hamed; Bilello, Michel; Davatzikos, Christos; Pohl, Kilian M.

    2014-01-01

    We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches. PMID:24595340

  6. Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

    NASA Astrophysics Data System (ADS)

    Jiji, G. Wiselin; Dehmeshki, Jamshid

    2014-04-01

    Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

  7. Remote Postoperative Epidural Hematoma after Brain Tumor Surgery

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2016-01-01

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

  9. Notching on Cancer's Door: Notch Signaling in Brain Tumors.

    PubMed

    Teodorczyk, Marcin; Schmidt, Mirko H H

    2014-01-01

    Notch receptors play an essential role in the regulation of central cellular processes during embryonic and postnatal development. The mammalian genome encodes for four Notch paralogs (Notch 1-4), which are activated by three Delta-like (Dll1/3/4) and two Serrate-like (Jagged1/2) ligands. Further, non-canonical Notch ligands such as epidermal growth factor like protein 7 (EGFL7) have been identified and serve mostly as antagonists of Notch signaling. The Notch pathway prevents neuronal differentiation in the central nervous system by driving neural stem cell maintenance and commitment of neural progenitor cells into the glial lineage. Notch is therefore often implicated in the development of brain tumors, as tumor cells share various characteristics with neural stem and progenitor cells. Notch receptors are overexpressed in gliomas and their oncogenicity has been confirmed by gain- and loss-of-function studies in vitro and in vivo. To this end, special attention is paid to the impact of Notch signaling on stem-like brain tumor-propagating cells as these cells contribute to growth, survival, invasion, and recurrence of brain tumors. Based on the outcome of ongoing studies in vivo, Notch-directed therapies such as γ-secretase inhibitors and blocking antibodies have entered and completed various clinical trials. This review summarizes the current knowledge on Notch signaling in brain tumor formation and therapy. PMID:25601901

  10. Training stem cells for treatment of malignant brain tumors

    PubMed Central

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

    2014-01-01

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

  11. Progress on the diagnosis and evaluation of brain tumors

    PubMed Central

    Gao, Huile

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

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

    PubMed

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

    2007-01-01

    Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. Second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract Spatio-Temporal Enhancement Patterns (STEPs). STEPs are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. While temporal enhancement features are extracted through Fourier transform, the resulting STEP framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant Gabor textures. High accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the ROC curve. PMID:18044593

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

    PubMed

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

    2015-02-01

    In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment. PMID:25575341

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

    NASA Astrophysics Data System (ADS)

    Yeo, Hangu; Sheinin, Vadim; Sheinin, Yuri

    2009-02-01

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

  16. Applicability of semi-automatic segmentation for volumetric analysis of brain lesions.

    PubMed

    Heinonen, T; Dastidar, P; Eskola, H; Frey, H; Ryymin, P; Laasonen, E

    1998-01-01

    This project involves the development of a fast semi-automatic segmentation procedure to make an accurate volumetric estimation of brain lesions. This method has been applied in the segmentation of demyelination plaques in Multiple Sclerosis (MS) and right cerebral hemispheric infarctions in patients with neglect. The developed segmentation method includes several image processing techniques, such as image enhancement, amplitude segmentation, and region growing. The entire program operates on a PC-based computer and applies graphical user interfaces. Twenty three patients with MS and 43 patients with right cerebral hemisphere infarctions were studied on a 0.5 T MRI unit. The MS plaques and cerebral infarctions were thereafter segmented. The volumetric accuracy of the program was demonstrated by segmenting Magnetic Resonance (MR) images of fluid filled syringes. The relative error of the total volume measurement based on the MR images of syringes was 1.5%. Also the repeatability test was carried out as inter-and intra-observer study in which MS plaques of six randomly selected patients were segmented. These tests indicated 7% variability in the inter-observer study and 4% variability in the intra-observer study. Average time used to segment and calculate the total plaque volumes for one patient was 10 min. This simple segmentation method can be utilized in the quantitation of anatomical structures, such as air cells in the sinonasal and temporal bone area, as well as in different pathological conditions, such as brain tumours, intracerebral haematomas and bony destructions. PMID:9680601

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

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

    PubMed

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

    2015-05-26

    Differential diagnosis of brain magnetic resonance imaging (MRI) enhancement(s) remains a significant problem, which may be difficult to resolve without biopsy, which can be often dangerous or even impossible. Such MRI enhancement(s) can result from metastasis of primary tumors such as lung or breast, radiation necrosis, infections, or a new primary brain tumor (glioma, meningioma). Neurological symptoms are often the same on initial presentation. To develop a more precise noninvasive MRI diagnostic method, we have engineered a new class of poly(β-l-malic acid) polymeric nanoimaging agents (NIAs). The NIAs carrying attached MRI tracer are able to pass through the blood-brain barrier (BBB) and specifically target cancer cells for efficient imaging. A qualitative/quantitative "MRI virtual biopsy" method is based on a nanoconjugate carrying MRI contrast agent gadolinium-DOTA and antibodies recognizing tumor-specific markers and extravasating through the BBB. In newly developed double tumor xenogeneic mouse models of brain metastasis this noninvasive method allowed differential diagnosis of HER2- and EGFR-expressing brain tumors. After MRI diagnosis, breast and lung cancer brain metastases were successfully treated with similar tumor-targeted nanoconjugates carrying molecular inhibitors of EGFR or HER2 instead of imaging contrast agent. The treatment resulted in a significant increase in animal survival and markedly reduced immunostaining for several cancer stem cell markers. Novel NIAs could be useful for brain diagnostic MRI in the clinic without currently performed brain biopsies. This technology shows promise for differential MRI diagnosis and treatment of brain metastases and other pathologies when biopsies are difficult to perform. PMID:25906400

  19. Robust automated detection, segmentation, and classification of hepatic tumors from CT data

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

    The manuscript presents the automated detection and segmentation of hepatic tumors from abdominal CT images with variable acquisition parameters. After obtaining an initial segmentation of the liver, optimized graph cuts segment the liver tumor candidates using shape and enhancement constraints. One hundred and fifty-seven features are computed for the tumor candidates and support vector machines are used to select features and separate true and false detections. Training and testing are performed using leave-one-patientout on 14 patients with a total of 79 tumors. After selection, the feature space is reduced to eight. The resulting sensitivity for tumor detection was 100% at 2.3 false positives/case. For the true tumors, 74.1% overlap and 1.6mm average surface distance were recorded between the ground truth and the results of the automated method. Results from test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow the diagnoses and temporal monitoring of patients with hepatic cancer.

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

    NASA Astrophysics Data System (ADS)

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

    2004-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  2. American brain tumor patients treated with BNCT in Japan

    SciTech Connect

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

    1995-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Veiseh, Omid

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

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

    NASA Astrophysics Data System (ADS)

    Wu, Xuqiang; Bricq, Stéphanie; Collet, Christophe

    2011-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1997-05-01

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

  6. Multiple Subsets of Brain Tumor Initiating Cells Coexist in Glioblastoma.

    PubMed

    Rennert, Robert C; Achrol, Achal S; Januszyk, Michael; Kahn, Suzana A; Liu, Tiffany T; Liu, Yi; Sahoo, Debashis; Rodrigues, Melanie; Maan, Zeshaan N; Wong, Victor W; Cheshier, Samuel H; Chang, Steven D; Steinberg, Gary K; Harsh, Griffith R; Gurtner, Geoffrey C

    2016-06-01

    Brain tumor-initiating cells (BTICs) are self-renewing multipotent cells critical for tumor maintenance and growth. Using single-cell microfluidic profiling, we identified multiple subpopulations of BTICs coexisting in human glioblastoma, characterized by distinct surface marker expression and single-cell molecular profiles relating to divergent bulk tissue molecular subtypes. These data suggest BTIC subpopulation heterogeneity as an underlying source of intra-tumoral bulk tissue molecular heterogeneity, and will support future studies into BTIC subpopulation-specific therapies. Stem Cells 2016;34:1702-1707. PMID:26991945

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2004-05-01

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

  10. Epigenetics in Brain Tumors: HDACs Take Center Stage

    PubMed Central

    Eyüpoglu, Ilker Y.; Savaskan, Nicolai E.

    2016-01-01

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

  11. Brain Tumors - Multiple Languages: MedlinePlus

    MedlinePlus

    ... page, please enable JavaScript. French (français) Japanese (日本語) Korean (한국어) Russian (Русский) Somali (af Soomaali) Spanish (español) ... 脳スキャン検査 - 日本語 (Japanese) Bilingual PDF Health Information Translations Korean (한국어) Brain Scan 뇌 스캔 - 한국어 (Korean) Bilingual ...

  12. II. Perinatal brain tumors: a review of 250 cases.

    PubMed

    Isaacs, Hart

    2002-11-01

    Central nervous system tumors occur considerably less often in the fetus and neonate than in the older child. They are not entirely the same as those present later in life. Their location, biologic behavior, response to therapy, and histologic types are different. Fetal and neonatal brain tumors (n = 250) were collected from the literature and studied for this review. The overall survival rate was 28%. The entire cranial cavity may be filled with tumor, and stillbirth is not uncommon. Macrocephaly was the most frequent presentation regardless of histology. Outcome is related to the size and location of the tumor, the histologic type, surgical resectability, and the condition of the infant at the time of diagnosis. Neonates with choroid plexus papillomas, gangliogliomas, and low-grade astrocytomas have the best prognosis, whereas those with teratomas and primitive neuroectodermal tumors have the worst prognosis. PMID:12504200

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

    PubMed

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

    2014-01-01

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

  14. Automatic segmentation of breast tumor in ultrasound image with simplified PCNN and improved fuzzy mutual information

    NASA Astrophysics Data System (ADS)

    Shi, Jun; Xiao, Zhiheng; Zhou, Shichong

    2010-07-01

    Image segmentation is very important in the field of image processing. The pulse coupled neural network (PCNN) has been efficiently applied to image processing, especially for image segmentation. In this study, a simplified PCNN (S-PCNN) model is proposed, the fuzzy mutual information (FMI) is improved as optimization criterion for S-PCNN, and then the S-PCNN and improved FMI (IFMI) based segmentation algorithm is proposed and applied for the segmentation of breast tumor in ultrasound image. To validate the proposed algorithm, a comparative experiment is implemented to segment breast images not only by our proposed algorithm, but also by the improved C-V algorithm, the max-entropy-based PCNN algorithm, the MI-based PCNN algorithm, and the IFMI-based PCNN algorithm. The results show that the breast lesions are well segmented by the proposed algorithm without image preprocessing, with the mean Hausdorff of distance of 5.631+/-0.822, mean average minimum Euclidean distance of 0.554+/-0.049, mean Tanimoto coefficient of 0.961+/-0.019, and mean misclassified error of 0.038+/-0.004. These values of evaluation indices are better than those of other segmentation algorithms. The results indicate that the proposed algorithm has excellent segmentation accuracy and strong robustness against noise, and it has the potential for breast ultrasound computer-aided diagnosis (CAD).

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  17. Life Satisfaction in Adult Survivors of Childhood Brain Tumors

    PubMed Central

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

    2014-01-01

    Adult survivors of childhood brain tumors experience multiple, significant, life-long deficits as a consequence of their malignancy and therapy. Current survivorship literature documents the substantial impact such impairments have on survivors’ physical health and quality of life. Psychosocial reports detail educational, cognitive, and emotional limitations characterizing survivors as especially fragile, often incompetent, and unreliable in evaluating their circumstances. Anecdotal data suggests some survivors report life experiences similar to those of healthy controls. The aim of our investigation was to determine whether life satisfaction in adult survivors of childhood brain tumors differs from that of healthy controls and to identify potential predictors of life satisfaction in survivors. This cross-sectional study compared 78 brain tumor survivors with population–based matched controls. Chi-square tests, t-tests, and linear regression models were used to investigate patterns of life satisfaction and identify potential correlates. Results indicated life satisfaction of adult survivors of childhood brain tumors was similar to that of healthy controls. Survivors’ general health expectations emerged as the primary correlate of life satisfaction. Understanding life satisfaction as an important variable will optimize the design of strategies to enhance participation in follow-up care, reduce suffering, and optimize quality of life in this vulnerable population. PMID:25027187

  18. Association Between PARP1 Single Nucleotide Polymorphism and Brain Tumors.

    PubMed

    Wang, Hong; Zhang, Kun; Qin, Haifeng; Yang, Lin; Zhang, Liyu; Cao, Yanyan

    2016-05-01

    To systematically evaluate the association between poly(ADP-ribose) polymerase 1 (PARP1) rs1136410 T>C and brain tumor risk, a meta-analysis has been carried out. We performed a meta-analysis of 2004 brain tumor patients and 2944 controls by use of STATA version 12.0 to determine whether the risk of brain tumors was associated with the genotypes or alleles of rs1136410 T>C. We found a significantly decreased risk (ranging from 0.18- to 0.16-fold) in the dominant model (OR = 0.84, 95 % CI = 0.75-0.95), the C vs. T model (OR = 0.82, 95 % CI = 0.74-0.91), and the CT vs. TT model (OR = 0.86, 95 % CI = 0.76-0.98). The same genetic models demonstrated noteworthy associations when analysis was restrained to glioma (OR = 0.85, 95 % CI = 0.75-0.96; OR = 0.83, 95 % CI = 0.74-0.92; OR = 0.87, 95 % CI = 0.76-0.99, respectively). This meta-analysis suggests that PARP1 rs1136410 T>C may play a significant role in the protection against the development of brain tumors and glioma. PMID:25911198

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

    MedlinePlus

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

  20. Learning Profiles of Survivors of Pediatric Brain Tumors

    ERIC Educational Resources Information Center

    Barkon, Beverly

    2009-01-01

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

  1. Genetic abnormality predicts benefit for a rare brain tumor

    Cancer.gov

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

  2. Tumor bed radiosurgery: an emerging treatment for brain metastases.

    PubMed

    Amsbaugh, Mark J; Boling, Warren; Woo, Shiao

    2015-06-01

    While typically used for treating small intact brain metastases, an increasing body of literature examining tumor bed directed stereotactic radiosurgery (SRS) is emerging. There are now over 1000 published cases treated with this approach, and the first prospective trial was recently published. The ideal sequencing of tumor bed SRS is unclear. Current approaches include, a neoadjuvant treatment before resection, alone as an adjuvant after resection, and following surgery combined with whole brain radiotherapy either as an adjuvant or salvage treatment. Based on available evidence, adjuvant stereotactic radiosurgery improves local control following surgery, reduces the number of patients who require whole brain radiotherapy, and is well tolerated. While results from published series vary, heterogeneity in both patient populations and methods of reporting results make comparisons difficult. Additional prospective data, including randomized trials are needed to confirm equivalent outcomes to the current standard of care. We review the current literature, identify areas of ongoing contention, and highlight ongoing studies. PMID:25911296

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  4. Interstitial hyperthermia of experimental brain tumor using implant heating system.

    PubMed

    Kobayashi, T; Tanaka, T; Kida, Y; Matsui, M; Ikeda, T

    1989-07-01

    New experimental system of induction hyperthermia for brain tumor using ferromagnetic implant with low Curie point has been developed. The metal implant is cylindrical needle and made of Fe-Pt alloy with low Curie point suitable for hyperthermia (50-60 degrees C). Induction coil and generator which produce maximum power of 200W and variable frequency of 100-500kHz, yielding magnetic power of 16.7Oe, have been developed. Interstitial hyperthermia was made on rat brain tumor model (T9 gliosarcoma) by this system. Significant effects of single hyperthermia (45 degrees C for 30 minutes) were observed by the extension of life span and morphological changes of the tumor. PMID:2778493

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

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

    PubMed Central

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

    2013-01-01

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

  8. Model-based segmentation of individual brain structures from MRI data

    NASA Astrophysics Data System (ADS)

    Collins, D. Louis; Peters, Terence M.; Dai, Weiqian; Evans, Alan C.

    1992-09-01

    This paper proposes a methodology that enables an arbitrary 3-D MRI brain image-volume to be automatically segmented and classified into neuro-anatomical components using multiresolution registration and matching with a novel volumetric brain structure model (VBSM). This model contains both raster and geometric data. The raster component comprises the mean MRI volume after a set of individual volumes of normal volunteers have been transformed to a standardized brain-based coordinate space. The geometric data consists of polyhedral objects representing anatomically important structures such as cortical gyri and deep gray matter nuclei. The method consists of iteratively registering the data set to be segmented to the VBSM using deformations based on local image correlation. This segmentation process is performed hierarchically in scale-space. Each step in decreasing levels of scale refines the fit of the previous step and provides input to the next. Results from phantom and real MR data are presented.

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

    PubMed

    Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

    2015-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  12. Generalized expectation-maximization segmentation of brain MR images

    NASA Astrophysics Data System (ADS)

    Devalkeneer, Arnaud A.; Robe, Pierre A.; Verly, Jacques G.; Phillips, Christophe L. M.

    2006-03-01

    Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation- Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with three for the image background). We have thus implemented an algorithm that combines the features of these two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al. Our combined approach leads to more robust and accurate segmentation.

  13. Segmentation of neonatal brain MR images using patch-driven level sets.

    PubMed

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

    2014-01-01

    The segmentation of neonatal brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), is challenging due to the low spatial resolution, severe partial volume effect, high image noise, and dynamic myelination and maturation processes. Atlas-based methods have been widely used for guiding neonatal brain segmentation. Existing brain atlases were generally constructed by equally averaging all the aligned template images from a population. However, such population-based atlases might not be representative of a testing subject in the regions with high inter-subject variability and thus often lead to a low capability in guiding segmentation in those regions. Recently, patch-based sparse representation techniques have been proposed to effectively select the most relevant elements from a large group of candidates, which can be used to generate a subject-specific representation with rich local anatomical details for guiding the segmentation. Accordingly, in this paper, we propose a novel patch-driven level set method for the segmentation of neonatal brain MR images by taking advantage of sparse representation techniques. Specifically, we first build a subject-specific atlas from a library of aligned, manually segmented images by using sparse representation in a patch-based fashion. Then, the spatial consistency in the probability maps from the subject-specific atlas is further enforced by considering the similarities of a patch with its neighboring patches. Finally, the probability maps are integrated into a coupled level set framework for more accurate segmentation. The proposed method has been extensively evaluated on 20 training subjects using leave-one-out cross validation, and also on 132 additional testing subjects. Our method achieved a high accuracy of 0.919±0.008 for white matter and 0.901±0.005 for gray matter, respectively, measured by Dice ratio for the overlap between the automated and manual segmentations in the cortical region

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

    PubMed

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

    2015-08-01

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

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

    SciTech Connect

    Adam, Jean-Francois

    2005-04-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  18. Radiation treatment of brain tumors: Concepts and strategies

    SciTech Connect

    Marks, J.E. )

    1989-01-01

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

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

    PubMed Central

    Ferraioli, Giampaolo; Pascazio, Vito

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-04-01

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

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

    PubMed Central

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

    2016-01-01

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

  4. 3D+t brain MRI segmentation using robust 4D Hidden Markov Chain.

    PubMed

    Lavigne, François; Collet, Christophe; Armspach, Jean-Paul

    2014-01-01

    In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account the temporal correlation in addition to spatial neighbourhood information. To improve the robustness of the segmentation of the principal brain structures and to detect Multiple Sclerosis Lesions as outliers the Trimmed Likelihood Estimator (TLE) is used during the process. The method is validated on 3D+t brain MR images. PMID:25571045

  5. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

    PubMed Central

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-01-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

  6. Optical spectroscopy for stereotactic biopsy of brain tumors

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  7. High incidence of TERT mutation in brain tumor cell lines.

    PubMed

    Johanns, Tanner M; Fu, Yujie; Kobayashi, Dale K; Mei, Yu; Dunn, Ian F; Mao, Diane D; Kim, Albert H; Dunn, Gavin P

    2016-07-01

    TERT promoter gene mutations are highly recurrent in malignant glioma. However, little information exists regarding their presence in experimental brain tumor models. To better characterize systems in which TERT mutation studies could be appropriately modeled experimentally, the TERT promoter was examined by conventional sequencing in primary brain tumor initiating cells (BTIC), two matched recurrent BTIC lines, a panel of established malignant glioma cell lines, and two meningioma cell lines. Telomerase gene expression was examined by quantitative PCR. We found that all glioblastoma BTIC lines harbored a TERT mutation, which was retained in two patient-matched recurrent BTIC. The TERT C228T or C250T mutation was found in 33/35 (94 %) of established malignant glioma cell lines and both meningioma cell lines examined. Brain tumor cell lines expressed variably high telomerase levels. Thus, a high percentage of glioma cell lines, as well as two meningioma cell lines, harbors TERT mutations. These data characterize tractable, accessible models with which to further explore telomerase biology in these tumor types. PMID:26960334

  8. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

    PubMed Central

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; Pareto, Deborah; Vilanova, Joan C.; Ramió-Torrentà, Lluís; Sastre-Garriga, Jaume; Montalban, Xavier; Rovira, Àlex; Lladó, Xavier

    2015-01-01

    Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations. PMID:26740917

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

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

    PubMed Central

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

    2015-01-01

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

  11. Topology polymorphism graph for lung tumor segmentation in PET-CT images.

    PubMed

    Cui, Hui; Wang, Xiuying; Zhou, Jianlong; Eberl, Stefan; Yin, Yong; Feng, Dagan; Fulham, Michael

    2015-06-21

    Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a 'topo-poly' graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an 'isolated' group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a 'complex' group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice's similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881 ± 0.046 and HD of 5.311 ± 3.022 mm for the isolated cases and DSC of 0.870 ± 0.038 and HD of 9.370 ± 3.169 mm for the complex cases. Student's t-test showed that our model outperformed the other methods (p-values <0.05). PMID:26056866

  12. Constrained Gaussian mixture model framework for automatic segmentation of MR brain images.

    PubMed

    Greenspan, Hayit; Ruf, Amit; Goldberger, Jacob

    2006-09-01

    An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains. PMID:16967808

  13. Permanent and removable implants for the brachytherapy of brain tumors

    SciTech Connect

    Gutin, P.H.; Phillips, T.L.; Hosobuchi, Y.

    1981-10-01

    Thirty-seven patients harboring primary or metastatic brain tumors were treated with 40 implantations of radioactive sources (/sup 192/Ir, /sup 198/Au, or /sup 125/I) using stereotactic neurosurgical techniques. Most tumors had recurred after surgery, whole brain irradiation, and treatment with all feasible chemotherapeutic agents. Sixteen of the 40 implants were pregnant; 24 were mounted in plastic catheters for removal after the desired dose had been delivered. One or more sources were placed in each tumor to deliver 3500-7350 rad to the tumor's periphery for /sup 198/Au, 4,000-12,000 rad for /sup 192/Ir, and 3,000-20,000 rad for /sup 125/I. Three of the six patients treated with /sup 192/Ir had objective responses for 2, 4, and 12 months, and two stabilized for 8 and 11 months. Seven of the 11 patients treated with /sup 198/Au were evaluable: three responded for 3, 5, and 37 + months, one deteriorating patient with a recurrent tumor stabilized for 6 months, and two deteriorated despite treatment. One patient received an interstitial ''boost'' dose with /sup 198/Au after whole brain irradiation and stabilized for 15 months before developing spinal metastases. Six patients received permanent implants with low activity /sup 125/I. Three of these patients had blioblastomas or anaplastic astrocytomas; all continued to deteriorate despite the interstitial irradiation, presumably because the dose rat was too low. One patient with a low-grade astrocytoma (optic chiasm) responded dramatically to permanent, low activity /sup 125/I implants (11 + months). Another (hypothalamic glioma) had a permanent /sup 125/I implant, responded, as was stable at 9 months when external irradiation was administered. One patient with a suprasellar ''teratoid'' tumor stabilized for 10 months.

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

    PubMed

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

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results. PMID:25571035

  15. Histogram analysis of ADC in brain tumor patients

    NASA Astrophysics Data System (ADS)

    Banerjee, Debrup; Wang, Jihong; Li, Jiang

    2011-03-01

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

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

    SciTech Connect

    Aristophanous, M; Yang, J; Beadle, B

    2014-06-01

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

  17. Pediatric Brain Tumors: Current Knowledge and Therapeutic Opportunities.

    PubMed

    Glod, John; Rahme, Gilbert J; Kaur, Harpreet; H Raabe, Eric; Hwang, Eugene I; Israel, Mark A

    2016-05-01

    Great progress has been made in many areas of pediatric oncology. However, tumors of the central nervous system (CNS) remain a significant challenge. A recent explosion of data has led to an opportunity to understand better the molecular basis of these diseases and is already providing a foundation for the pursuit of rationally chosen therapeutics targeting relevant molecular pathways. The molecular biology of pediatric brain tumors is shifting from a singular focus on basic scientific discovery to a platform upon which insights are being translated into therapies. PMID:26989915

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

    PubMed Central

    Sukheeja, Deepti; Mehta, Jayanti

    2016-01-01

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

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

    PubMed

    Sukheeja, Deepti; Mehta, Jayanti

    2016-01-01

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

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

    ClinicalTrials.gov

    2016-05-17

    Childhood Atypical Teratoid/Rhabdoid Tumor; Childhood Central Nervous System Germ Cell Tumor; Childhood Choroid Plexus Tumor; Childhood Craniopharyngioma; Childhood Grade I Meningioma; Childhood Grade II Meningioma; Childhood Grade III Meningioma; Childhood High-grade Cerebral Astrocytoma; Childhood Infratentorial Ependymoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Oligodendroglioma; Childhood Supratentorial Ependymoma; Newly Diagnosed Childhood Ependymoma; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Childhood Visual Pathway and Hypothalamic Glioma; Recurrent Childhood Visual Pathway Glioma

  1. A skull segmentation method for brain MR images based on multiscale bilateral filtering scheme

    NASA Astrophysics Data System (ADS)

    Yang, Xiaofeng; Fei, Baowei

    2010-03-01

    We present a novel automatic segmentation method for the skull on brain MR images for attenuation correction in combined PET/MRI applications. Our method transforms T1-weighted MR images to the Radon domain and then detects the feature of the skull. In the Radon domain we use a bilateral filter to construct a multiscale images series. For the repeated convolution we increase the spatial smoothing at each scale and make the cumulative width of the spatial and range Gaussian doubled at each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The method is robust for noise MR images because of its multiscale bilateral filtering scheme. After combining the two filtered sinogram, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image. We use the filtered back projection method to reconstruct the segmented skull image. We define six metrics to evaluate our segmentation method. The method has been tested with brain phantom data, simulated brain data, and real MRI data. Evaluation results showed that our method is robust and accurate, which is useful for skull segmentation and subsequently for attenuation correction in combined PET/MRI applications.

  2. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation

    PubMed Central

    Roy, Snehashis; Carass, Aaron; Pacheco, Jennifer; Bilgel, Murat; Resnick, Susan M.; Prince, Jerry L.; Pham, Dzung L.

    2016-01-01

    Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4–12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis. PMID:26958465

  3. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

    PubMed Central

    Mendrik, Adriënne M.; Vincken, Koen L.; Kuijf, Hugo J.; Breeuwer, Marcel; Bouvy, Willem H.; de Bresser, Jeroen; Alansary, Amir; de Bruijne, Marleen; Carass, Aaron; El-Baz, Ayman; Jog, Amod; Katyal, Ranveer; Khan, Ali R.; van der Lijn, Fedde; Mahmood, Qaiser; Mukherjee, Ryan; van Opbroek, Annegreet; Paneri, Sahil; Pereira, Sérgio; Rajchl, Martin; Sarikaya, Duygu; Smedby, Örjan; Silva, Carlos A.; Vrooman, Henri A.; Vyas, Saurabh; Wang, Chunliang; Zhao, Liang; Biessels, Geert Jan; Viergever, Max A.

    2015-01-01

    Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand. PMID:26759553

  4. Robust kernelized local information fuzzy C-means clustering for brain magnetic resonance image segmentation.

    PubMed

    Elazab, Ahmed; AbdulAzeem, Yousry M; Wu, Shiqian; Hu, Qingmao

    2016-03-17

    Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity. PMID:27257884

  5. Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy

    PubMed Central

    Lu, Chao; Chelikani, Sudhakar; Jaffray, David A.; Milosevic, Michael F.; Staib, Lawrence H.; Duncan, James S.

    2013-01-01

    External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician. PMID:22328178

  6. Computer applications to radioactive-seed: Brain-tumor implants

    SciTech Connect

    Meli, J.A.; Dicker, C.S.; Schulz, R.J.

    1989-05-01

    Malignant brain tumors, in general, and anaplastic astrocytoma and glioblastoma multiforme in particular, have been highly refractory to conventional treatments including surgery, chemotherapy and external-beam irradiation. Although better local control can be achieved with high-dose, external beam irradiation, necrosis of normal brain tissue reduces the quality of life and survival. In order to localize the radiation dose given to brain tumors, the temporary implantation of /sup 125/I and /sup 192/Ir seeds is undergoing clinical trials at several medical centers. Computers play a key role in this treatment modality: in addition to being essential for image reconstruction of CT scans, a computer is used to reconstruct a tumor volume from outlined regions on individual cuts; a programable calculator is used in conjunction with a stereotaxic head holder to obtain the coordinates of the radioactive seeds; a radiation-therapy, treatment-planning computer is used to optimize the radioactive-seed positions and strengths, and to generate the corresponding dose distribution.

  7. Sigma and opioid receptors in human brain tumors

    SciTech Connect

    Thomas, G.E.; Szuecs, M.; Mamone, J.Y.; Bem, W.T.; Rush, M.D.; Johnson, F.E.; Coscia, C.J. )

    1990-01-01

    Human brain tumors and nude mouse-borne human neuroblastomas and gliomas were analyzed for sigma and opioid receptor content. Sigma binding was assessed using ({sup 3}H) 1, 3-di-o-tolylguanidine (DTG), whereas opioid receptor subtypes were measured with tritiated forms of the following: {mu}, (D-ala{sup 2}, mePhe{sup 4}, gly-ol{sup 5}) enkephalin (DAMGE); {kappa}, ethylketocyclazocine (EKC) or U69,593; {delta}, (D-pen{sup 2}, D-pen{sup 5}) enkephalin (DPDPE) or (D-ala{sup 2}, D-leu{sup 5}) enkephalin (DADLE) with {mu} suppressor present. Binding parameters were estimated by homologous displacement assays followed by analysis using the LIGAND program. Sigma binding was detected in 15 of 16 tumors examined with very high levels found in a brain metastasis from an adenocarcinoma of lung and a human neuroblastoma (SK-N-MC) passaged in nude mice. {kappa} opioid receptor binding was detected in 4 of 4 glioblastoma multiforme specimens and 2 of 2 human astrocytoma cell lines tested but not in the other brain tumors analyzed.

  8. Heavy Metals and Epigenetic Alterations in Brain Tumors

    PubMed Central

    Caffo, Maria; Caruso, Gerardo; Fata, Giuseppe La; Barresi, Valeria; Visalli, Maria; Venza, Mario; Venza, Isabella

    2014-01-01

    Heavy metals and their derivatives can cause various diseases. Numerous studies have evaluated the possible link between exposure to heavy metals and various cancers. Recent data show a correlation between heavy metals and aberration of genetic and epigenetic patterns. From a literature search we noticed few experimental and epidemiological studies that evaluate a possible correlation between heavy metals and brain tumors. Gliomas arise due to genetic and epigenetic alterations of glial cells. Changes in gene expression result in the alteration of the cellular division process. Epigenetic alterations in brain tumors include the hypermethylation of CpG group, hypomethylation of specific genes, aberrant activation of genes, and changes in the position of various histones. Heavy metals are capable of generating reactive oxygen assumes that key functions in various pathological mechanisms. Alteration of homeostasis of metals could cause the overproduction of reactive oxygen species and induce DNA damage, lipid peroxidation, and alteration of proteins. In this study we summarize the possible correlation between heavy metals, epigenetic alterations and brain tumors. We report, moreover, the review of relevant literature. PMID:25646073

  9. Heavy metals and epigenetic alterations in brain tumors.

    PubMed

    Caffo, Maria; Caruso, Gerardo; Fata, Giuseppe La; Barresi, Valeria; Visalli, Maria; Venza, Mario; Venza, Isabella

    2014-12-01

    Heavy metals and their derivatives can cause various diseases. Numerous studies have evaluated the possible link between exposure to heavy metals and various cancers. Recent data show a correlation between heavy metals and aberration of genetic and epigenetic patterns. From a literature search we noticed few experimental and epidemiological studies that evaluate a possible correlation between heavy metals and brain tumors. Gliomas arise due to genetic and epigenetic alterations of glial cells. Changes in gene expression result in the alteration of the cellular division process. Epigenetic alterations in brain tumors include the hypermethylation of CpG group, hypomethylation of specific genes, aberrant activation of genes, and changes in the position of various histones. Heavy metals are capable of generating reactive oxygen assumes that key functions in various pathological mechanisms. Alteration of homeostasis of metals could cause the overproduction of reactive oxygen species and induce DNA damage, lipid peroxidation, and alteration of proteins. In this study we summarize the possible correlation between heavy metals, epigenetic alterations and brain tumors. We report, moreover, the review of relevant literature. PMID:25646073

  10. Recent progress towards development of effective systemic chemotherapy for the treatment of malignant brain tumors

    PubMed Central

    Sarin, Hemant

    2009-01-01

    Systemic chemotherapy has been relatively ineffective in the treatment of malignant brain tumors even though systemic chemotherapy drugs are small molecules that can readily extravasate across the porous blood-brain tumor barrier of malignant brain tumor microvasculature. Small molecule systemic chemotherapy drugs maintain peak blood concentrations for only minutes, and therefore, do not accumulate to therapeutic concentrations within individual brain tumor cells. The physiologic upper limit of pore size in the blood-brain tumor barrier of malignant brain tumor microvasculature is approximately 12 nanometers. Spherical nanoparticles ranging between 7 nm and 10 nm in diameter maintain peak blood concentrations for several hours and are sufficiently smaller than the 12 nm physiologic upper limit of pore size in the blood-brain tumor barrier to accumulate to therapeutic concentrations within individual brain tumor cells. Therefore, nanoparticles bearing chemotherapy that are within the 7 to 10 nm size range can be used to deliver therapeutic concentrations of small molecule chemotherapy drugs across the blood-brain tumor barrier into individual brain tumor cells. The initial therapeutic efficacy of the Gd-G5-doxorubicin dendrimer, an imageable nanoparticle bearing chemotherapy within the 7 to 10 nm size range, has been demonstrated in the orthotopic RG-2 rodent malignant glioma model. Herein I discuss this novel strategy to improve the effectiveness of systemic chemotherapy for the treatment of malignant brain tumors and the therapeutic implications thereof. PMID:19723323

  11. Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

    PubMed

    Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

    2016-01-01

    Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

  12. Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

    PubMed Central

    Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

    2016-01-01

    Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

  13. Atlas-based segmentation of deep brain structures using non-rigid registration

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Faisal; Mewes, Klaus; Gross, Robert E.; Škrinjar, Oskar

    2008-03-01

    Deep brain structures are frequently used as targets in neurosurgical procedures. However, the boundaries of these structures are often not visible in clinically used MR and CT images. Techniques based on anatomical atlases and indirect targeting are used to infer the location of these targets intraoperatively. Initial errors of such approaches may be up to a few millimeters, which is not negligible. E.g. subthalamic nucleus is approximately 4x6 mm in the axial plane and the diameter of globus pallidus internus is approximately 8 mm, both of which are used as targets in deep brain stimulation surgery. To increase the initial localization accuracy of deep brain structures we have developed an atlas-based segmentation method that can be used for the surgery planning. The atlas is a high resolution MR head scan of a healthy volunteer with nine deep brain structures manually segmented. The quality of the atlas image allowed for the segmentation of the deep brain structures, which is not possible from the clinical MR head scans of patients. The subject image is non-rigidly registered to the atlas image using thin plate splines to represent the transformation and normalized mutual information as a similarity measure. The obtained transformation is used to map the segmented structures from the atlas to the subject image. We tested the approach on five subjects. The quality of the atlas-based segmentation was evaluated by visual inspection of the third and lateral ventricles, putamena, and caudate nuclei, which are visible in the subject MR images. The agreement of these structures for the five tested subjects was approximately 1 to 2 mm.

  14. Automated Brain Tissue Segmentation Based on Fractional Signal Mapping from Inversion Recovery Look-Locker Acquisition

    PubMed Central

    Shin, Wanyong; Geng, Xiujuan; Gu, Hong; Zhan, Wang; Zou, Qihong; Yang, Yihong

    2010-01-01

    Most current automated segmentation methods are performed on T1- or T2-weighted MR images, relying on relative image intensity that is dependent on other MR parameters and sensitive to B1 magnetic field inhomogeneity. Here, we propose an image segmentation method based on quantitative longitudinal magnetization relaxation time (T1) of brain tissues. Considering the partial volume effect, fractional volume maps of brain tissues (white matter, gray matter, and cerebrospinal fluid) were obtained by fitting the observed signal in an inversion recovery procedure to a linear combination of three exponential functions, which represents the relaxations of each of the tissue types. A Look-Locker acquisition was employed to accelerate the acquisition process. The feasibility and efficacy of this proposed method were evaluated using simulations and experiments. The potential applications of this method in the study of neurological disease as well as normal brain development and aging are discussed. PMID:20452444

  15. Segmentation of brain image volumes using the data list management library.

    PubMed

    Román-Alonso, G; Jiménez-Alaniz, J R; Buenabad-Chávez, J; Castro-García, M A; Vargas-Rodríguez, A H

    2007-01-01

    The segmentation of head images is useful to detect neuroanatomical structures and to follow and quantify the evolution of several brain lesions. 2D images correspond to brain slices. The more images are used the higher the resolution obtained is, but more processing power is required and parallelism becomes desirable. We present a new approach to segmentation of brain image volumes using DLML (Data List Management Library), a tool developed by our team. We organise the integer numbers identifying images into a list, and our DLML version process them both in parallel and with dynamic load balancing transparently to the programmer. We compare the performance of our DLML version to other typical parallel approaches developed with MPI (master-slave and static data distribution), using cluster configurations with 4-32 processors. PMID:18002398

  16. Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps

    PubMed Central

    Helms, Gunther; Draganski, Bogdan; Frackowiak, Richard; Ashburner, John; Weiskopf, Nikolaus

    2009-01-01

    Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures. PMID:19344771

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  18. Vasculature segmentation for radio frequency ablation of non-resectable hepatic tumors

    NASA Astrophysics Data System (ADS)

    Hemler, Paul F.; McCreedy, Evan S.; Cheng, Ruida; Wood, Brad; McAuliffe, Matthew J.

    2006-03-01

    In Radio Frequency Ablation (RFA) procedures, hepatic tumor tissue is heated to a temperature where necrosis is insured. Unfortunately, recent results suggest that heating tumor tissue to necrosis is complicated because nearby major blood vessels provide a cooling effect. Therefore, it is fundamentally important for physicians to perform a careful analysis of the spatial relationship of diseased tissue to larger liver blood vessels. The liver contains many of these large vessels, which affect the RFA ablation shape and size. There are many sophisticated vasculature detection and segmentation techniques reported in the literature that identify continuous vessels as the diameter changes size and it transgresses through many bifurcation levels. However, the larger blood vessels near the treatment area are the only vessels required for proper RFA treatment plan formulation and analysis. With physician guidance and interaction, our system can segment those vessels which are most likely to affect the RFA ablations. We have found that our system provides the physician with therapeutic, geometric and spatial information necessary to accurately plan treatment of tumors near large blood vessels. The segmented liver vessels near the treatment region are also necessary for computing isolevel heating profiles used to evaluate different proposed treatment configurations.

  19. Follow-up segmentation of lung tumors in PET and CT data

    NASA Astrophysics Data System (ADS)

    Opfer, Roland; Kabus, Sven; Schneider, Torben; Carlsen, Ingwer C.; Renisch, Steffen; Sabczynski, Jörg

    2009-02-01

    Early response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. We have developed algorithms which allow the user to track both tumor volume and standardized uptake value (SUV) measurements during the therapy from series of CT and PET images, respectively. To prepare for tumor volume estimation we have developed a new technique for a fast, flexible, and intuitive 3D definition of meshes. This initial surface is then automatically adapted by means of a model-based segmentation algorithm and propagated to each follow-up scan. If necessary, manual corrections can be added by the user. To determine SUV measurements a prioritized region growing algorithm is employed. For an improved workflow all algorithms are embedded in a PET/CT therapy monitoring software suite giving the clinician a unified and immediate access to all data sets. Whenever the user clicks on a tumor in a base-line scan, the courses of segmented tumor volumes and SUV measurements are automatically identified and displayed to the user as a graph plot. According to each course, the therapy progress can be classified as complete or partial response or as progressive or stable disease. We have tested our methods with series of PET/CT data from 9 lung cancer patients acquired at Princess Margaret Hospital in Toronto. Each patient underwent three PET/CT scans during a radiation therapy. Our results indicate that a combination of mean metabolic activity in the tumor with the PET-based tumor volume can lead to an earlier response detection than a purely volume based (CT diameter) or purely functional based (e.g. SUV max or SUV mean) response measures. The new software seems applicable for easy, faster, and reproducible quantification to routinely monitor tumor therapy.

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

    PubMed Central

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

    2014-01-01

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

  1. Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI

    PubMed Central

    Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

    2016-01-01

    This paper presents an automatic lesion segmentation method based on similarities between multichannel patches. A patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally an iterative patch-based label refinement process based on the initial segmentation map is performed to ensure the spatial consistency of the detected lesions. The method was evaluated in experiments on multiple sclerosis (MS) lesion segmentation in magnetic resonance images (MRI) of the brain. An evaluation was done for each image in the MICCAI 2008 MS lesion segmentation challenge. Results are shown to compete with the state of the art in the challenge. We conclude that the proposed algorithm for segmentation of lesions provides a promising new approach for local segmentation and global detection in medical images. PMID:26904103

  2. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI.

    PubMed

    Sauwen, Nicolas; Sima, Diana M; Van Cauter, Sofie; Veraart, Jelle; Leemans, Alexander; Maes, Frederik; Himmelreich, Uwe; Van Huffel, Sabine

    2015-12-01

    Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was

  3. Automated segmentation of brain ventricles in unenhanced CT of patients with ischemic stroke

    NASA Astrophysics Data System (ADS)

    Qian, Xiaohua; Wang, Jiahui; Li, Qiang

    2013-02-01

    We are developing an automated method for detection and quantification of ischemic stroke in computed tomography (CT). Ischemic stroke often connects to brain ventricle, therefore, ventricular segmentation is an important and difficult task when stroke is present, and is the topic of this study. We first corrected inclination angle of brain by aligning midline of brain with the vertical centerline of a slice. We then estimated the intensity range of the ventricles by use of the k-means method. Two segmentation of the ventricle were obtained by use of thresholding technique. One segmentation contains ventricle and nearby stroke. The other mainly contains ventricle. Therefore, the stroke regions can be extracted and removed using image difference technique. An adaptive template-matching algorithm was employed to identify objects in the fore-mentioned segmentation. The largest connected component was identified and considered as the ventricle. We applied our method to 25 unenhanced CT scans with stroke. Our method achieved average Dice index, sensitivity, and specificity of 95.1%, 97.0%, and 99.8% for the entire ventricular regions. The experimental results demonstrated that the proposed method has great potential in detection and quantification of stroke and other neurologic diseases.

  4. White Matter MS-Lesion Segmentation Using a Geometric Brain Model.

    PubMed

    Strumia, Maddalena; Schmidt, Frank R; Anastasopoulos, Constantinos; Granziera, Cristina; Krueger, Gunnar; Brox, Thomas

    2016-07-01

    Brain magnetic resonance imaging (MRI) in patients with Multiple Sclerosis (MS) shows regions of signal abnormalities, named plaques or lesions. The spatial lesion distribution plays a major role for MS diagnosis. In this paper we present a 3D MS-lesion segmentation method based on an adaptive geometric brain model. We model the topological properties of the lesions and brain tissues in order to constrain the lesion segmentation to the white matter. As a result, the method is independent of an MRI atlas. We tested our method on the MICCAI MS grand challenge proposed in 2008 and achieved competitive results. In addition, we used an in-house dataset of 15 MS patients, for which we achieved best results in most distances in comparison to atlas based methods. Besides classical segmentation distances, we motivate and formulate a new distance to evaluate the quality of the lesion segmentation, while being robust with respect to minor inconsistencies at the boundary level of the ground truth annotation. PMID:26829786

  5. Fast method for brain image segmentation: application to proton magnetic resonance spectroscopic imaging.

    PubMed

    Bonekamp, David; Horská, Alena; Jacobs, Michael A; Arslanoglu, Atilla; Barker, Peter B

    2005-11-01

    The interpretation of brain metabolite concentrations measured by quantitative proton magnetic resonance spectroscopic imaging (MRSI) is assisted by knowledge of the percentage of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) within each MRSI voxel. Usually, this information is determined from T(1)-weighted magnetic resonance images (MRI) that have a much higher spatial resolution than the MRSI data. While this approach works well, it is time-consuming. In this article, a rapid data acquisition and analysis procedure for image segmentation is described, which is based on collection of several, thick slice, fast spin echo images (FSE) of different contrast. Tissue segmentation is performed with linear "Eigenimage" filtering and normalization. The method was compared to standard segmentation techniques using high-resolution 3D T(1)-weighted MRI in five subjects. Excellent correlation between the two techniques was obtained, with voxel-wise regression analysis giving GM: R2 = 0.893 +/- 0.098, WM: R2 = 0.892 +/- 0.089, ln(CSF): R2 = 0.831 +/- 0.082). Test-retest analysis in one individual yielded an excellent agreement of measurements with R2 higher than 0.926 in all three tissue classes. Application of FSE/EI segmentation to a sample proton MRSI dataset yielded results similar to prior publications. It is concluded that FSE imaging in conjunction with Eigenimage analysis is a rapid and reliable way of segmenting brain tissue for application to proton MRSI. PMID:16187272

  6. 3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

    PubMed Central

    Zhang, Shaoting; Huang, Junzhou; Uzunbas, Mustafa; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora; Thanos, Panayotis; Metaxas, Dimitris N.

    2016-01-01

    In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance. PMID:22003750

  7. 3D segmentation of rodent brain structures using hierarchical shape priors and deformable models.

    PubMed

    Zhang, Shaoting; Huang, Junzhou; Uzunbas, Mustafa; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora; Thanos, Panayotis; Metaxas, Dimitris N

    2011-01-01

    In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance. PMID:22003750

  8. Simulating ‘structure-function’ patterns of malignant brain tumors

    NASA Astrophysics Data System (ADS)

    Mansury, Yuri; Deisboeck, Thomas S.

    2004-01-01

    Rapid growth and extensive tissue infiltration are characteristics of highly malignant neuroepithelial brain tumors. Very little is known, however, about the existence of structure-function relationships in these types of neoplasm. Therefore, using a previously developed two-dimensional agent-based model, we have investigated the emergent patterns of multiple tumor cells that proliferate and migrate on an adaptive grid lattice, driven by a local-search mechanism and guided by the presence of distinct environmental conditions. Numerical results indicate a strong correlation between the fractal dimensions of the tumor surface and the average velocity of the tumor's spatial expansion. In particular, when the so called ‘beaten-path advantage’ intensifies, i.e., rising ‘mechanical rewards’ for cells to follow each other along preformed pathways, it results in an increase of the tumor system's fractal dimensions leading to a concomitant acceleration of its spatial expansion. Whereas cell migration is the dominant phenotype responsible for the more extensive branching patterns exhibiting higher fractal dimensions, cell proliferation appears to become more active primarily at lower fracticality associated with stronger mechanical confinements. Implications of these results for experimental and clinical cancer research are discussed.

  9. Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features.

    PubMed

    Guo, Yanrong; Wu, Guorong; Commander, Leah A; Szary, Stephanie; Jewells, Valerie; Lin, Weili; Shent, Dinggang

    2014-01-01

    Accurate segmentation of the hippocampus from infant MR brain images is a critical step for investigating early brain development. Unfortunately, the previous tools developed for adult hippocampus segmentation are not suitable for infant brain images acquired from the first year of life, which often have poor tissue contrast and variable structural patterns of early hippocampal development. From our point of view, the main problem is lack of discriminative and robust feature representations for distinguishing the hippocampus from the surrounding brain structures. Thus, instead of directly using the predefined features as popularly used in the conventional methods, we propose to learn the latent feature representations of infant MR brain images by unsupervised deep learning. Since deep learning paradigms can learn low-level features and then successfully build up more comprehensive high-level features in a layer-by-layer manner, such hierarchical feature representations can be more competitive for distinguishing the hippocampus from entire brain images. To this end, we apply Stacked Auto Encoder (SAE) to learn the deep feature representations from both T1- and T2-weighed MR images combining their complementary information, which is important for characterizing different development stages of infant brains after birth. Then, we present a sparse patch matching method for transferring hippocampus labels from multiple atlases to the new infant brain image, by using deep-learned feature representations to measure the interpatch similarity. Experimental results on 2-week-old to 9-month-old infant brain images show the effectiveness of the proposed method, especially compared to the state-of-the-art counterpart methods. PMID:25485393

  10. Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

    PubMed

    Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

    2013-10-01

    Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems. PMID:23836390

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

    PubMed Central

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

    2015-01-01

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

  12. Patient-specific computational biomechanics of the brain without segmentation and meshing.

    PubMed

    Zhang, Johnny Y; Joldes, Grand Roman; Wittek, Adam; Miller, Karol

    2013-02-01

    Motivated by patient-specific computational modelling in the context of image-guided brain surgery, we propose a new fuzzy mesh-free modelling framework. The method works directly on an unstructured cloud of points that do not form elements so that mesh generation is not required. Mechanical properties are assigned directly to each integration point based on fuzzy tissue classification membership functions without the need for image segmentation. Geometric integration is performed over an underlying uniform background grid. The verification example shows that, while requiring no hard segmentation and meshing, the proposed model gives, for all practical purposes, equivalent results to a finite element model. PMID:23345159

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

    NASA Astrophysics Data System (ADS)

    O'Dell, Walter G.

    2012-03-01

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

  14. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    PubMed Central

    Jain, Saurabh; Sima, Diana M.; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D.; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

    2015-01-01

    The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter

  15. A New Method to Segment the Multiple Sclerosis Lesions on Brain Magnetic Resonance Images.

    PubMed

    Karimian, Alireza; Jafari, Simin

    2015-01-01

    Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow-up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1-weighted (T1-w), T2-w, and T2-fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation-maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time-consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1-w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST-trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time-consuming. PMID:26955567

  16. The role of IDO in brain tumor immunotherapy

    PubMed Central

    Zhai, Lijie; Lauing, Kristen L.; Chang, Alan L.; Dey, Mahua; Qian, Jun; Cheng, Yu; Lesniak, Maciej S.; Wainwright, Derek A.

    2015-01-01

    Malignant glioma comprises the majority of primary brain tumors. Coincidently, most of those malignancies express an inducible tryptophan catabolic enzyme, indoleamine 2,3 dioxygenase 1 (IDO1). While IDO1 is not normally expressed at appreciable levels in the adult central nervous system, it's rapidly induced and/or upregulated upon inflammatory stimulus. The primary function of IDO1 is associated with conversion of the essential amino acid, tryptophan, into downstream catabolites known as kynure-nines. The depletion of tryptophan and/or accumulation of kynurenine has been shown to induce T cell deactivation, apoptosis and/or the induction of immunosuppressive programming via the expression of FoxP3. This understanding has informed immunotherapeutic design for the strategic development of targeted molecular therapeutics that inhibit IDO1 activity. Here, we review the current knowledge of IDO1 in brain tumors, pre-clinical studies targeting this enzymatic pathway, alternative tryptophan catabolic mediators that compensate for IDO1 loss and/or inhibition, as well as proposed clinical strategies and questions that are critical to address for increasing future immunotherapeutic effectiveness in patients with incurable brain cancer. PMID:25519303

  17. Round Randomized Learning Vector Quantization for Brain Tumor Imaging

    PubMed Central

    2016-01-01

    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function. PMID:27516807

  18. Round Randomized Learning Vector Quantization for Brain Tumor Imaging.

    PubMed

    Sheikh Abdullah, Siti Norul Huda; Bohani, Farah Aqilah; Nayef, Baher H; Sahran, Shahnorbanun; Al Akash, Omar; Iqbal Hussain, Rizuana; Ismail, Fuad

    2016-01-01

    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function. PMID:27516807

  19. Boron Neutron Capture Therapy for Malignant Brain Tumors

    PubMed Central

    MIYATAKE, Shin-Ichi; KAWABATA, Shinji; HIRAMATSU, Ryo; KUROIWA, Toshihiko; SUZUKI, Minoru; KONDO, Natsuko; ONO, Koji

    2016-01-01

    Boron neutron capture therapy (BNCT) is a biochemically targeted radiotherapy based on the nuclear capture and fission reactions that occur when non-radioactive boron-10, which is a constituent of natural elemental boron, is irradiated with low energy thermal neutrons to yield high linear energy transfer alpha particles and recoiling lithium-7 nuclei. Therefore, BNCT enables the application of a high dose of particle radiation selectively to tumor cells in which boron-10 compound has been accumulated. We applied BNCT using nuclear reactors for 167 cases of malignant brain tumors, including recurrent malignant gliomas, newly diagnosed malignant gliomas, and recurrent high-grade meningiomas from January 2002 to May 2014. Here, we review the principle and history of BNCT. In addition, we introduce fluoride-18-labeled boronophenylalanine positron emission tomography and the clinical results of BNCT for the above-mentioned malignant brain tumors. Finally, we discuss the recent development of accelerators producing epithermal neutron beams. This development could provide an alternative to the current use of specially modified nuclear reactors as a neutron source, and could allow BNCT to be performed in a hospital setting. PMID:27250576

  20. Boron Neutron Capture Therapy for Malignant Brain Tumors.

    PubMed

    Miyatake, Shin-Ichi; Kawabata, Shinji; Hiramatsu, Ryo; Kuroiwa, Toshihiko; Suzuki, Minoru; Kondo, Natsuko; Ono, Koji

    2016-07-15

    Boron neutron capture therapy (BNCT) is a biochemically targeted radiotherapy based on the nuclear capture and fission reactions that occur when non-radioactive boron-10, which is a constituent of natural elemental boron, is irradiated with low energy thermal neutrons to yield high linear energy transfer alpha particles and recoiling lithium-7 nuclei. Therefore, BNCT enables the application of a high dose of particle radiation selectively to tumor cells in which boron-10 compound has been accumulated. We applied BNCT using nuclear reactors for 167 cases of malignant brain tumors, including recurrent malignant gliomas, newly diagnosed malignant gliomas, and recurrent high-grade meningiomas from January 2002 to May 2014. Here, we review the principle and history of BNCT. In addition, we introduce fluoride-18-labeled boronophenylalanine positron emission tomography and the clinical results of BNCT for the above-mentioned malignant brain tumors. Finally, we discuss the recent development of accelerators producing epithermal neutron beams. This development could provide an alternative to the current use of specially modified nuclear reactors as a neutron source, and could allow BNCT to be performed in a hospital setting. PMID:27250576

  1. Mitochondrial Control by DRP1 in Brain Tumor Initiating Cells

    PubMed Central

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

    2015-01-01

    Brain tumor initiating cells (BTICs) coopt the neuronal high affinity GLUT3 glucose transporter to withstand metabolic stress. Here, we investigated another mechanism critical to brain metabolism, mitochondrial morphology. BTICs displayed mitochondrial fragmentation relative to non-BTICs, suggesting that BTICs have increased mitochondrial fission. The essential mediator of mitochondrial fission, dynamin-related protein 1 (DRP1), was activated in BTICs and inhibited in non-BTICs. Targeting DRP1 using RNA interference or pharmacologic inhibition induced BTIC apoptosis and inhibited tumor growth. Downstream, DRP1 activity regulated the essential metabolic stress sensor, AMP-activated protein kinase (AMPK), and AMPK targeting rescued the effects of DRP1 disruption. Cyclin-dependent kinase 5 (CDK5) phosphorylated DRP1 to increase its activity in BTICs, whereas Ca2+–calmodulin-dependent protein kinase 2 (CAMK2) inhibited DRP1 in non-BTICs, suggesting tumor cell differentiation induces a regulatory switch in mitochondrial morphology. DRP1 activation correlates with poor prognosis in glioblastoma, suggesting mitochondrial dynamics may represent a therapeutic target for BTICs. PMID:25730670

  2. Exploratory case-control study of brain tumors in adults

    SciTech Connect

    Burch, J.D.; Craib, K.J.; Choi, B.C.; Miller, A.B.; Risch, H.A.; Howe, G.R.

    1987-04-01

    An exploratory study of brain tumors in adults was carried out using 215 cases diagnosed in Southern Ontario between 1979 and 1982, with an individually matched, hospital control series. Significantly elevated risks were observed for reported use of spring water, drinking of wine, and consumption of pickled fish, together with a significant protective effect for the regular consumption of any of several types of fruit. While these factors are consistent with a role for N-nitroso compounds in the etiology of these tumors, for several other factors related to this hypothesis, no association was observed. Occupation in the rubber industry was associated with a significant relative risk of 9.0, though no other occupational associations were seen. Two previously unreported associations were with smoking nonfilter cigarettes with a significant trend and with the use of hair dyes or sprays. The data do not support an association between physical head trauma requiring medical attention and risk of brain tumors and indicate that exposure to ionizing radiation and vinyl chloride monomer does not contribute any appreciable fraction of attributable risk in the population studied. The findings warrant further detailed investigation in future epidemiologic studies.

  3. Regional brain glucose metabolism in patients with brain tumors before and after radiotherapy

    SciTech Connect

    Wang, G.J.; Volkow, N.D.; Lau, Y.H.

    1994-05-01

    This study was performed to measure regional glucose metabolism in nonaffected brain regions of patients with primary or metastatic brain tumors. Seven female and four male patients (mean age 51.5{plus_minus}14.0 years old) were compared with eleven age and sex matched normal subjects. None of the patients had hydrocephalus and/or increased intracranial pressure. Brain glucose metabolism was measured using FDG-PET scan. Five of the patients were reevaluated one week after receiving radiation treatment (RT) to the brain. Patients were on Decadron and/or Dilantin at the time of both scan. PET images were analyzed with a template of 115 nonoverlapping regions of interest and then grouped into eight gray matter regions on each hemisphere. Brain regions with tumors and edema shown in MR imaging were excluded. Z scores were used to compare individual patients` regional values with those of normal subjects. The number of regional values with Z scores of less than - 3.0 were considered abnormal and were quantified. The mean global glucose metabolic rate (mean of all regions) in nonaffected brain regions of patients was significantly lower than that of normal controls (32.1{plus_minus}9.0 versus 44.8{plus_minus}6.3 {mu}mol/100g/min, p<0.001). Analyses of individual subjects revealed that none of the controls and 8 of the 11 patients had at least one abnormal region. In these 8 patients the regions which were abnormal were most frequently localized in right (n=5) and left occipital (n=6) and right orbital frontal cortex (n=7) whereas the basal ganglia was not affected. Five of the patients who had repeated scans following RT showed decrements in tumor metabolism (41{plus_minus}20.5%) and a significant increase in whole brain metabolism (8.6{plus_minus}5.3%, p<0.001). The improvement in whole brain metabolism after RT suggests that the brain metabolic decrements in the patients were related to the presence of tumoral tissue and not just a medication effect.

  4. Brain Penetration and Efficacy of Different Mebendazole Polymorphs in a Mouse Brain Tumor Model

    PubMed Central

    Wanjiku, Teresia; Rudek, Michelle A; Joshi, Avadhut; Gallia, Gary L.; Riggins, Gregory J.

    2015-01-01

    Purpose Mebendazole (MBZ), first used as an antiparasitic drug, shows preclinical efficacy in models of glioblastoma and medulloblastoma. Three different MBZ polymorphs (A, B and C) exist and a detailed assessment of the brain penetration, pharmacokinetics and anti-tumor properties of each individual MBZ polymorph is necessary to improve mebendazole-based brain cancer therapy. Experimental Design and Results In this study, various marketed and custom-formulated MBZ tablets were analyzed for their polymorph content by IR spectroscopy and subsequently tested in orthotopic GL261 mouse glioma model for efficacy and tolerability. The pharmacokinetics and brain concentration of MBZ polymorphs and two main metabolites were analyzed by LC-MS. We found that polymorph B and C both increased survival in a GL261 glioma model, as B exhibited greater toxicity. Polymorph A showed no benefit. Both, polymorph B and C, reached concentrations in the brain that exceeded the IC50 in GL261 cells 29-fold. In addition, polymorph C demonstrated an AUC0-24h brain-to-plasma (B/P) ratio of 0.82, whereas B showed higher plasma AUC and lower B/P ratio. In contrast, polymorph A presented markedly lower levels in the plasma and brain. Furthermore, the combination with elacridar was able to significantly improve the efficacy of polymorph C in GL261 glioma and D425 medulloblastoma models in mice. Conclusion Among MBZ polymorphs, C reaches therapeutically effective concentrations in the brain tissue and tumor with less side effects and is the better choice for brain cancer therapy. Its efficacy can be further enhanced by combination with elacridar. PMID:25862759

  5. Topology polymorphism graph for lung tumor segmentation in PET-CT images

    NASA Astrophysics Data System (ADS)

    Cui, Hui; Wang, Xiuying; Zhou, Jianlong; Eberl, Stefan; Yin, Yong; Feng, Dagan; Fulham, Michael

    2015-06-01

    Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a ‘topo-poly’ graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an ‘isolated’ group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a ‘complex’ group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice’s similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881  ±  0.046 and HD of 5.311  ±  3.022 mm for the isolated cases and DSC of 0.870  ±  0.038 and HD of 9.370  ±  3.169 mm for the complex cases. Student’s t-test showed that our model outperformed the other methods (p-values <0.05).

  6. Contrast medium accumulation and washout in canine brain tumors and irradiated normal brain: a CT study of kinetics

    SciTech Connect

    Fike, J.R.; Cann, C.E.

    1984-04-01

    Kinetics of an iodinated contrast medium were evaluated quantitatively as a function of time up to one hour after intravenous infusion in the brains of dogs with experimentally induced radiation damage and dogs with spontaneous brain tumor. Radiation damage was characterized by an increase in iodine accumulation soon after the infusion, while tumor concentration of iodine either showed no change or decreased with time. These results suggest that contrast kinetic studies may be useful in differentiating radiation damage to normal brain tissue from a malignant brain tumor.

  7. Statistical feature selection for enhanced detection of brain tumor

    NASA Astrophysics Data System (ADS)

    Chaddad, Ahmad; Colen, Rivka R.

    2014-09-01

    Feature-based methods are widely used in the brain tumor recognition system. Robust of early cancer detection is one of the most powerful image processing tools. Specifically, statistical features, such as geometric mean, harmonic mean, mean excluding outliers, median, percentiles, skewness and kurtosis, have been extracted from brain tumor glioma to aid in discriminating two levels namely, Level I and Level II using fluid attenuated inversion recovery (FLAIR) sequence in the diagnosis of brain tumor. Statistical feature describes the major characteristics of each level from glioma which is an important step to evaluate heterogeneity of cancer area pixels. In this paper, we address the task of feature selection to identify the relevant subset of features in the statistical domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between Level I and Level II. We apply a Decision Structure algorithm to find the optimal combination of nonhomogeneity based statistical features for the problem at hand. We employ a Naïve Bayes classifier to evaluate the performance of the optimal statistical feature based scheme in terms of its glioma Level I and Level II discrimination capability and use real-data collected from 17 patients have a glioblastoma multiforme (GBM). Dataset provided from 3 Tesla MR imaging system by MD Anderson Cancer Center. For the specific data analyzed, it is shown that the identified dominant features yield higher classification accuracy, with lower number of false alarms and missed detections, compared to the full statistical based feature set. This work has been proposed and analyzed specific GBM types which Level I and Level II and the dominant features were considered as feature aid to prognostic indicators. These features were selected automatically to be better able to determine prognosis from classical imaging studies.

  8. Identifying the needs of brain tumor patients and their caregivers.

    PubMed

    Parvataneni, Rupa; Polley, Mei-Yin; Freeman, Teresa; Lamborn, Kathleen; Prados, Michael; Butowski, Nicholas; Liu, Raymond; Clarke, Jennifer; Page, Margaretta; Rabbitt, Jane; Fedoroff, Anne; Clow, Emelia; Hsieh, Emily; Kivett, Valerie; Deboer, Rebecca; Chang, Susan

    2011-09-01

    The purpose of this study is to identify the needs of brain tumor patients and their caregivers to provide improved health services to these populations. Two different questionnaires were designed for patients and caregivers. Both questionnaires contained questions pertaining to three realms: disease symptoms/treatment, health care provider, daily living/finances. The caregivers' questionnaires contained an additional domain on emotional needs. Each question was evaluated for the degree of importance and satisfaction. Exploratory analyses determined whether baseline characteristics affect responder importance or satisfaction. Also, areas of high agreement/disagreement in satisfaction between the participating patient-caregiver pairs were identified. Questions for which >50% of the patients and caregivers thought were "very important" but >30% were dissatisfied include: understanding the cause of brain tumors, dealing with patients' lower energy, identifying healthful foods and activities for patients, telephone access to health care providers, information on medical insurance coverage, and support from their employer. In the emotional realm, caregivers identified 9 out of 10 items as important but need further improvement. Areas of high disagreement in satisfaction between participating patient-caregiver pairs include: getting help with household chores (P value = 0.006) and finding time for personal needs (P value < 0.001). This study provides insights into areas to improve services for brain tumor patients and their caregivers. The caregivers' highest amount of burden is placed on their emotional needs, emphasizing the importance of providing appropriate medical and psychosocial support for caregivers to cope with emotional difficulties they face during the patients' treatment process. PMID:21311950

  9. Tumor Directed, Scalp Sparing Intensity Modulated Whole Brain Radiotherapy for Brain Metastases.

    PubMed

    Kao, Johnny; Darakchiev, Boramir; Conboy, Linda; Ogurek, Sara; Sharma, Neha; Ren, Xuemin; Pettit, Jeffrey

    2015-10-01

    Despite significant technical advances in radiation delivery, conventional whole brain radiation therapy (WBRT) has not materially changed in the past 50 years. We hypothesized that IMRT can selectively spare uninvolved brain and scalp with the goal of reducing acute and late toxicity. MRI/CT simulation image registration was performed. We performed IMRT planning to simultaneously treat the brain tumor(s) on MRI + 5 mm margin to 37.5 Gy in 15 fractions while limiting the uninvolved brain + 2 mm margin to 30 Gy in 15 fractions and the mean scalp dose to #18 Gy. Three field IMRT plans were compared to conventional WBRT plans. Symptomatic patients were started on conventional WBRT for 2 to 3 fractions while IMRT planning was performed. Seventeen consecutive patients with brain metastases with RPA class I and II disease with no leptomeningeal spread were treated with IMRT WBRT. Compared to conventional WBRT, IMRT reduced the mean scalp dose (26.2 Gy vs. 16.4 Gy, p < 0.001) and the mean PTV30 dose (38.4 Gy vs. 32.0 Gy, p < 0.001) while achieving similar mean PTV37.5 doses (38.3 Gy vs. 38.0 Gy, p = 0.26). Using Olsen hair loss score criteria, 4 of 15 assessable patients preserved at least 50% of hair coverage at 1 to 3 months after treatment while 6 patients preserved between 25 and 50% hair coverage. At a median follow-up of 6.8 months (range: 5 to 15 months), the median overall survival was 5.4 months. Four patients relapsed within the brain, one within the PTV37.5 and three outside the PTV37.5. Tumor directed, scalp sparing IMRT is feasible, achieves rational dose distributions and preserves partial hair coverage in the majority of patients. Further studies are warranted to determine whether the increased utilization of resources needed for IMRT are appropriate in this setting. PMID:24750002

  10. Three-Staged Stereotactic Radiotherapy Without Whole Brain Irradiation for Large Metastatic Brain Tumors

    SciTech Connect

    Higuchi, Yoshinori Serizawa, Toru; Nagano, Osamu; Matsuda, Shinji; Ono, Junichi; Sato, Makoto; Iwadate, Yasuo; Saeki, Naokatsu

    2009-08-01

    Purpose: To evaluate the efficacy and toxicity of staged stereotactic radiotherapy with a 2-week interfraction interval for unresectable brain metastases more than 10 cm{sup 3} in volume. Patients and Methods: Subjects included 43 patients (24 men and 19 women), ranging in age from 41 to 84 years, who had large brain metastases (> 10 cc in volume). Primary tumors were in the colon in 14 patients, lung in 12, breast in 11, and other in 6. The peripheral dose was 10 Gy in three fractions. The interval between fractions was 2 weeks. The mean tumor volume before treatment was 17.6 {+-} 6.3 cm{sup 3} (mean {+-} SD). Mean follow-up interval was 7.8 months. The local tumor control rate, as well as overall, neurological, and qualitative survivals, were calculated using the Kaplan-Meier method. Results: At the time of the second and third fractions, mean tumor volumes were 14.3 {+-} 6.5 (18.8% reduction) and 10.6 {+-} 6.1 cm{sup 3} (39.8% reduction), respectively, showing significant reductions. The median overall survival period was 8.8 months. Neurological and qualitative survivals at 12 months were 81.8% and 76.2%, respectively. Local tumor control rates were 89.8% and 75.9% at 6 and 12 months, respectively. Tumor recurrence-free and symptomatic edema-free rates at 12 months were 80.7% and 84.4%, respectively. Conclusions: The 2-week interval allowed significant reduction of the treatment volume. Our results suggest staged stereotactic radiotherapy using our protocol to be a possible alternative for treating large brain metastases.

  11. Collective Behavior of Brain Tumor Cells: the Role of Hypoxia

    NASA Astrophysics Data System (ADS)

    Khain, Evgeniy; Katakowski, Mark; Hopkins, Scott; Szalad, Alexandra; Zheng, Xuguang; Jiang, Feng; Chopp, Michael

    2013-03-01

    We consider emergent collective behavior of a multicellular biological system. Specifically we investigate the role of hypoxia (lack of oxygen) in migration of brain tumor cells. We performed two series of cell migration experiments. The first set of experiments was performed in a typical wound healing geometry: cells were placed on a substrate, and a scratch was done. In the second set of experiments, cell migration away from a tumor spheroid was investigated. Experiments show a controversy: cells under normal and hypoxic conditions have migrated the same distance in the ``spheroid'' experiment, while in the ``scratch'' experiment cells under normal conditions migrated much faster than under hypoxic conditions. To explain this paradox, we formulate a discrete stochastic model for cell dynamics. The theoretical model explains our experimental observations and suggests that hypoxia decreases both the motility of cells and the strength of cell-cell adhesion. The theoretical predictions were further verified in independent experiments.

  12. Brain tumor CT attenuation coefficients: semiquantitative analysis of histograms.

    PubMed

    Ratzka, M; Haubitz, I

    1983-01-01

    This paper reports on work in progress on semiquantitative curve analyses of histograms of brain tumors. Separation of statistical groups of attenuation values obtained by computer calculation is done separately from scanning, using histogram printouts as the data input for a programmable calculator. This method is discussed together with its results in 50 cases of malignant gliomas. The detection of hidden tissue portions and the more accurate evaluation of partial enhancement effects have been the investigators' main concerns to the present time; however, this method may allow more specific diagnosis of malignancy and changes in tumor characteristics than visual assessment alone. This has not been proven by studies that have evaluated large numbers of cases, but seems to be worth pursuing as a new approach. PMID:6410783

  13. Histone H3 Mutations in Pediatric Brain Tumors

    PubMed Central

    Liu, Xiaoyang; McEachron, Troy A.; Schwartzentruber, Jeremy; Wu, Gang

    2014-01-01

    Until recently, mutations in histones had not been described in any human disease. However, genome-wide sequencing of pediatric high-grade gliomas revealed somatic heterozygous mutations in the genes encoding histones H3.1 and H3.3, as well as mutations in the chromatin modifiers ATRX and DAXX. The functional significance and mechanistic details of how these mutations affect the tumors is currently under intensive investigation. The information gained from these studies will shed new light on normal brain development as well as increase our understanding of the tumorigenic processes that drive pediatric high-grade gliomas. PMID:24691963

  14. Absence of human cytomegalovirus infection in childhood brain tumors.

    PubMed

    Sardi, Iacopo; Lucchesi, Maurizio; Becciani, Sabrina; Facchini, Ludovica; Guidi, Milena; Buccoliero, Anna Maria; Moriondo, Maria; Baroni, Gianna; Stival, Alessia; Farina, Silvia; Genitori, Lorenzo; de Martino, Maurizio

    2015-01-01

    Human cytomegalovirus (HCMV) is a common human pathogen which induces different clinical manifestations related to the age and the immune conditions of the host. HCMV infection seems to be involved in the pathogenesis of adult glioblastomas. The aim of our study was to detect the presence of HCMV in high grade gliomas and other pediatric brain tumors. This hypothesis might have important therapeutic implications, offering a new target for adjuvant therapies. Among 106 pediatric patients affected by CNS tumors we selected 27 patients with a positive HCMV serology. The serological analysis revealed 7 patients with positive HCMV IGG (≥14 U/mL), whom had also a high HCMV IgG avidity, suggesting a more than 6 months-dated infection. Furthermore, HCMV IGM were positive (≥22 U/mL) in 20 patients. Molecular and immunohistochemical analyses were performed in all the 27 samples. Despite a positive HCMV serology, confirmed by ELISA, no viral DNA was shown at the PCR analysis in the patients' neoplastic cells. At immunohistochemistry, no expression of HCMV antigens was observed in tumoral cells. Our results are in agreement with recent results in adults which did not evidence the presence of HCMV genome in glioblastoma lesions. We did not find any correlation between HCMV infection and pediatric CNS tumors. PMID:26396923

  15. Absence of human cytomegalovirus infection in childhood brain tumors

    PubMed Central

    Sardi, Iacopo; Lucchesi, Maurizio; Becciani, Sabrina; Facchini, Ludovica; Guidi, Milena; Buccoliero, Anna Maria; Moriondo, Maria; Baroni, Gianna; Stival, Alessia; Farina, Silvia; Genitori, Lorenzo; de Martino, Maurizio

    2015-01-01

    Human cytomegalovirus (HCMV) is a common human pathogen which induces different clinical manifestations related to the age and the immune conditions of the host. HCMV infection seems to be involved in the pathogenesis of adult glioblastomas. The aim of our study was to detect the presence of HCMV in high grade gliomas and other pediatric brain tumors. This hypothesis might have important therapeutic implications, offering a new target for adjuvant therapies. Among 106 pediatric patients affected by CNS tumors we selected 27 patients with a positive HCMV serology. The serological analysis revealed 7 patients with positive HCMV IGG (≥14 U/mL), whom had also a high HCMV IgG avidity, suggesting a more than 6 months-dated infection. Furthermore, HCMV IGM were positive (≥22 U/mL) in 20 patients. Molecular and immunohistochemical analyses were performed in all the 27 samples. Despite a positive HCMV serology, confirmed by ELISA, no viral DNA was shown at the PCR analysis in the patients’ neoplastic cells. At immunohistochemistry, no expression of HCMV antigens was observed in tumoral cells. Our results are in agreement with recent results in adults which did not evidence the presence of HCMV genome in glioblastoma lesions. We did not find any correlation between HCMV infection and pediatric CNS tumors. PMID:26396923

  16. A Method to Automate the Segmentation of the GTV and ITV for Lung Tumors

    SciTech Connect

    Ehler, Eric D.; Bzdusek, Karl; Tome, Wolfgang A.

    2009-07-01

    Four-dimensional computed tomography (4D-CT) is a useful tool in the treatment of tumors that undergo significant motion. To fully utilize 4D-CT motion information in the treatment of mobile tumors such as lung cancer, autosegmentation methods will need to be developed. Using autosegmentation tools in the Pinnacle{sup 3} v8.1t treatment planning system, 6 anonymized 4D-CT data sets were contoured. Two test indices were developed that can be used to evaluate which autosegmentation tools to apply to a given gross tumor volume (GTV) region of interest (ROI). The 4D-CT data sets had various phase binning error levels ranging from 3% to 29%. The appropriate autosegmentation method (rigid translational image registration and deformable surface mesh) was determined to properly delineate the GTV in all of the 4D-CT phases for the 4D-CT data sets with binning errors of up to 15%. The ITV was defined by 2 methods: a mask of the GTV in all 4D-CT phases and the maximum intensity projection. The differences in centroid position and volume were compared with manual segmentation studies in literature. The indices developed in this study, along with the autosegmentation tools in the treatment planning system, were able to automatically segment the GTV in the four 4D-CTs with phase binning errors of up to 15%.

  17. Segments.

    ERIC Educational Resources Information Center

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

    2001-01-01

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

  18. Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach

    PubMed Central

    Gu, Yuhua; Kumar, Virendra; Hall, Lawrence O; Goldgof, Dmitry B; Li, Ching-Yen; Korn, René; Bendtsen, Claus; Velazquez, Emmanuel Rios; Dekker, Andre; Aerts, Hugo; Lambin, Philippe; Li, Xiuli; Tian, Jie; Gatenby, Robert A; Gillies, Robert J

    2012-01-01

    A single click ensemble segmentation (SCES) approach based on an existing “Click&Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated. PMID:23459617

  19. The modern brain tumor operating room: from standard essentials to current state-of-the-art.

    PubMed

    Barnett, Gene H; Nathoo, Narendra

    2004-01-01

    It is just over a century since successful brain tumor resection. Since then the diagnosis, imaging, and management of brain tumors have improved, in large part due to technological advances. Similarly, the operating room (OR) for brain tumor surgery has increased in complexity and specificity with multiple forms of equipment now considered necessary as technical adjuncts. It is evident that the theme of minimalism in combination with advanced image-guidance techniques and a cohort of sophisticated technologies (e.g., robotics and nanotechnology) will drive changes in the current OR environment for the foreseeable future. In this report we describe what may be regarded today as standard essentials in an operating room for the surgical management of brain tumors and what we believe to be the current 'state-of-the-art' brain tumor OR. Also, we speculate on the additional capabilities of the brain tumor OR of the near future. PMID:15527078

  20. Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

    PubMed Central

    Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

    2010-01-01

    The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

  1. [Untoward side effects of chemoradiotherapy in children with malignant brain tumors].

    PubMed

    Morozova, S K; Begun, I V; Spivak, L V; Radiuk, K A; Papkevich, I I; Savich, T V; Pershaĭ, E B; Vashkevich, T I; Aleĭnikova, O V

    2002-01-01

    Untoward side-effects of chemoradiotherapy were compared in 48 children treated for brain tumors and those in remission lasting from less than 12 months to 11 years. The investigation concerned disturbances in the neurologic, endocrine, cardiovascular, urinary, hepatobiliary and psychic systems; neurologic ones proved the most frequent. No cases of heart failure were reported among patients with brain tumors during remission. Hormonal study revealed inhibited thyroid function in brain tumor sufferers. PMID:12455363

  2. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy

    PubMed Central

    Ji, Minbiao; Lewis, Spencer; Camelo-Piragua, Sandra; Ramkissoon, Shakti H.; Snuderl, Matija; Venneti, Sriram; Fisher-Hubbard, Amanda; Garrard, Mia; Fu, Dan; Wang, Anthony C.; Heth, Jason A.; Maher, Cormac O.; Sanai, Nader; Johnson, Timothy D.; Freudiger, Christian W.; Sagher, Oren; Xie, Xiaoliang Sunney; Orringer, Daniel A.

    2016-01-01

    Differentiating tumor from normal brain is a major barrier to achieving optimal outcome in brain tumor surgery. New imaging techniques for visualizing tumor margins during surgery are needed to improve surgical results. We recently demonstrated the ability of stimulated Raman scattering (SRS) microscopy, a non-destructive, label-free optical method, to reveal glioma infiltration in animal models. Here we show that SRS reveals human brain tumor infiltration in fresh, unprocessed surgical specimens from 22 neurosurgical patients. SRS detects tumor infiltration in near-perfect agreement with standard hematoxylin and eosin light microscopy (κ=0.86). The unique chemical contrast specific to SRS microscopy enables tumor detection by revealing quantifiable alterations in tissue cellularity, axonal density and protein:lipid ratio in tumor-infiltrated tissues. To ensure that SRS microscopic data can be easily used in brain tumor surgery, without the need for expert interpretation, we created a classifier based on cellularity, axonal density and protein:lipid ratio in SRS images capable of detecting tumor infiltration with 97.5% sensitivity and 98.5% specificity. Importantly, quantitative SRS microscopy detects the spread of tumor cells, even in brain tissue surrounding a tumor that appears grossly normal. By accurately revealing tumor infiltration, quantitative SRS microscopy holds potential for improving the accuracy of brain tumor surgery. PMID:26468325

  3. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy.

    PubMed

    Ji, Minbiao; Lewis, Spencer; Camelo-Piragua, Sandra; Ramkissoon, Shakti H; Snuderl, Matija; Venneti, Sriram; Fisher-Hubbard, Amanda; Garrard, Mia; Fu, Dan; Wang, Anthony C; Heth, Jason A; Maher, Cormac O; Sanai, Nader; Johnson, Timothy D; Freudiger, Christian W; Sagher, Oren; Xie, Xiaoliang Sunney; Orringer, Daniel A

    2015-10-14

    Differentiating tumor from normal brain is a major barrier to achieving optimal outcome in brain tumor surgery. New imaging techniques for visualizing tumor margins during surgery are needed to improve surgical results. We recently demonstrated the ability of stimulated Raman scattering (SRS) microscopy, a nondestructive, label-free optical method, to reveal glioma infiltration in animal models. We show that SRS reveals human brain tumor infiltration in fresh, unprocessed surgical specimens from 22 neurosurgical patients. SRS detects tumor infiltration in near-perfect agreement with standard hematoxylin and eosin light microscopy (κ = 0.86). The unique chemical contrast specific to SRS microscopy enables tumor detection by revealing quantifiable alterations in tissue cellularity, axonal density, and protein/lipid ratio in tumor-infiltrated tissues. To ensure that SRS microscopic data can be easily used in brain tumor surgery, without the need for expert interpretation, we created a classifier based on cellularity, axonal density, and protein/lipid ratio in SRS images capable of detecting tumor infiltration with 97.5% sensitivity and 98.5% specificity. Quantitative SRS microscopy detects the spread of tumor cells, even in brain tissue surrounding a tumor that appears grossly normal. By accurately revealing tumor infiltration, quantitative SRS microscopy holds potential for improving the accuracy of brain tumor surgery. PMID:26468325

  4. Segmental testicular infarction in a young man simulating a testicular tumor.

    PubMed

    Kim, Hee Kyung; Goske, Marilyn J; Bove, Kevin E; Minovich, Eugene

    2009-04-01

    A 19-year-old boy presented with a 48-hour history of acute onset severe right scrotal pain with minimal scrotal swelling. High-frequency US including color Doppler demonstrated a wedge-shaped, heterogeneous, avascular testicular mass diagnosed preoperatively as a segmental testicular infarction (STI). This was proved at surgery and subsequent histology. The preoperative diagnosis of STI was suggested based on the young man's presentation of severe pain and the sonographic appearance of the mass. Entertaining the preoperative diagnosis of STI from a testicular tumor is important for testis-sparing surgery even though STI in the pediatric age group is extremely rare. PMID:19214495

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

    MedlinePlus

    ... brain and spinal cord tumors in children What’s new in research and treatment for brain and spinal ... an investigational method, and studies are continuing. Other new treatment strategies Researchers are also testing some newer ...

  6. Description of brain internal structures by means of spatial relations for MR image segmentation

    NASA Astrophysics Data System (ADS)

    Colliot, Olivier; Camara, Oscar; Dewynter, Remi; Bloch, Isabelle

    2004-05-01

    This paper presents a method for segmenting internal brain structures in MR images. It introduces prior information in an original way through descriptions of the spatial arrangement of structures by means of spatial relations, which are represented in the fuzzy set framework. The method is hierarchical as the segmentation of a given structure is based on the previously segmented ones. The processing of each structure is decomposed into two stages: an initialization stage which makes extensive use of prior knowledge and a refinement stage using a 3D deformable model. The deformable model is guided by an external force representing the combination of a classical data term derived from an edge map and a force corresponding to a given spatial relation. We propose different ways to compute a force from a fuzzy set representing a relation or a combination of relations. Results obtained for the lateral ventricles, the third ventricle, the caudate nuclei and the thalami are promising. The proposed combination of spatial relations and deformable models has proved to be very useful to segment parts of the structures were no visible edges are present, improving the segmentation accuracy.

  7. SEMI-AUTOMATIC SEGMENTATION OF BRAIN SUBCORTICAL STRUCTURES FROM HIGH-FIELD MRI

    PubMed Central

    Kim, Jinyoung; Lenglet, Christophe; Sapiro, Guillermo; Harel, Noam

    2015-01-01

    Volumetric segmentation of subcortical structures such as the basal ganglia and thalamus is necessary for non-invasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. This paper presents a semi-automatic segmentation system exploiting the superior image quality of ultra-high field (7 Tesla) MRI. The proposed approach handles and exploits multiple structural MRI modalities. It uniquely combines T1-weighted (T1W), T2-weighted (T2W), diffusion, and susceptibility-weighted (SWI) MRI and introduces a dedicated new edge indicator function. In addition to this, we employ prior shape and configuration knowledge of the subcortical structures in order to guide the evolution of geometric active surfaces. Neighboring structures are segmented iteratively, constraining over-segmentation at their borders with a non-overlapping penalty. Extensive experiments with data acquired on a 7T MRI scanner demonstrate the feasibility and power of the approach for the segmentation of basal ganglia components critical for neurosurgery applications such as deep brain stimulation. PMID:25192576

  8. Cryptococcal Brainstem Abscess Mimicking Brain Tumors in an Immunocompetent Patient

    PubMed Central

    Hur, Jong Hee; Kim, Jang-Hee; Park, Seoung Woo

    2015-01-01

    Usually fungal infections caused by opportunistic and pathogenic fungi had been an important cause of morbidity and mortality among immunocompromised patients. However clinical data and investigations for immunocompetent pathogenic fungal infections had been rare and neglected into clinical studies. Especially Cryptococcal brainstem abscess cases mimicking brain tumors were also much more rare. So we report this unusual case. This 47-year-old man presented with a history of progressively worsening headache and nausea for 1 month and several days of vomituritions before admission. Neurological and laboratory examinations performed demonstrated no abnormal findings. Previously he was healthy and did not have any significant medical illnesses. A CT and MRI scan revealed enhancing 1.8×1.7×2.0 cm mass lesion in the left pons having central necrosis and peripheral edema compressing the fourth ventricle. And also positron emission tomogram scan demonstrated a hot uptake of fluoro-deoxy-glucose on the brainstem lesion without any evidences of systemic metastasis. Gross total mass resection was achieved with lateral suboccipital approach with neuronavigation system. Postoperatively he recovered without any neurological deficits. Pathologic report confirmed Cryptococcus neoformans and he was successively treated with antifungal medications. This is a previously unreported rare case of brainstem Cryptococcal abscess mimicking brain tumors in immunocompetent host without having any apparent typical meningeal symptoms and signs with resultant good neurosurgical recovery. PMID:25674344

  9. Analgesic use and the risk of primary adult brain tumor.

    PubMed

    Egan, Kathleen M; Nabors, Louis B; Thompson, Zachary J; Rozmeski, Carrie M; Anic, Gabriella A; Olson, Jeffrey J; LaRocca, Renato V; Chowdhary, Sajeel A; Forsyth, Peter A; Thompson, Reid C

    2016-09-01

    Glioma and meningioma are uncommon tumors of the brain with few known risk factors. Regular use of aspirin has been linked to a lower risk of gastrointestinal and other cancers, though evidence for an association with brain tumors is mixed. We examined the association of aspirin and other analgesics with the risk of glioma and meningioma in a large US case-control study. Cases were persons recently diagnosed with glioma or meningioma and treated at medical centers in the southeastern US. Controls were persons sampled from the same communities as the cases combined with friends and other associates of the cases. Information on past use of analgesics (aspirin, other anti-inflammatory agents, and acetaminophen) was collected in structured interviews. Logistic regression was used to estimate odds ratios (ORs) and 95 % confidence intervals (CIs) for analgesic use adjusted for potential confounders. All associations were considered according to indication for use. A total of 1123 glioma cases, 310 meningioma cases and 1296 controls were included in the analysis. For indications other than headache, glioma cases were less likely than controls to report regular use of aspirin (OR 0.69; CI 0.56, 0.87), in a dose-dependent manner (P trend < 0.001). No significant associations were observed with other analgesics for glioma, or any class of pain reliever for meningioma. Results suggest that regular aspirin use may reduce incidence of glioma. PMID:26894804

  10. Is outpatient brain tumor surgery feasible in India?

    PubMed

    Turel, Mazda K; Bernstein, Mark

    2016-01-01

    The current trend in all fields of surgery is towards less invasive procedures with shorter hospital stays. The reasons for this change include convenience to patients, optimal resource utilization, and cost saving. Technological advances in neurosurgery, aided by improvements in anesthesia, have resulted in surgery that is faster, simpler, and safer with excellent perioperative recovery. As a result of improved outcomes, some centers are performing brain tumor surgery on an outpatient basis, wherein patients arrive at the hospital the morning of their procedure and leave the hospital the same evening, thus avoiding an overnight stay in the hospital. In addition to the medical benefits of the outpatient procedure, its impact on patient satisfaction is substantial. The economic benefits are extremely favorable for the patient, physician, as well as the hospital. In high volume centers, a day surgery program can exist alongside those for elective and emergency surgeries, providing another pathway for patient care. However, due to skepticism surrounding the medicolegal aspects, and how radical the concept at first sounds, these procedures have not gained widespread popularity. We provide an overview of outpatient brain tumor surgery in the western world, discussing the socioeconomic, medicolegal, and ethical issues related to its adaptability in a developing nation. PMID:27625225

  11. Computer-Aided Segmentation of the Mid-Brain in Trans-Cranial Ultrasound Images.

    PubMed

    Sakalauskas, Andrius; Laučkaitė, Kristina; Lukoševičius, Arūnas; Rastenytė, Daiva

    2016-01-01

    This paper presents a novel and rapid method developed for semi-automated segmentation of the mid-brain region in B-mode trans-cranial ultrasound (TCS) images. TCS is a relatively new neuroimaging tool having promising application in early diagnosis of Parkinson's disease. The quality of TCS images is much lower compared with the ultrasound images obtained during scanning of the soft tissues; the structures of interest in TCS are difficult to extract and to evaluate. The combination of an experience-based statistical shape model and intensity-amplitude invariant edge detector was proposed for the extraction of fuzzy boundaries of the mid-brain in TCS images. A statistical shape model was constructed using 90 manual delineations of the mid-brain region made by professional neurosonographer. Local phase-based edge detection strategy was applied for determination of plausible mid-brain boundary points used for statistical shape fitting. The proposed method was tested on other 40 clinical TCS images evaluated by two experts. The obtained averaged results of segmentation revealed that the differences between manual and automated measurements are statistically insignificant (p > 0.05). PMID:26603659

  12. Fetal brain tumors: Prenatal diagnosis by ultrasound and magnetic resonance imaging

    PubMed Central

    Milani, Hérbene José; Araujo Júnior, Edward; Cavalheiro, Sérgio; Oliveira, Patrícia Soares; Hisaba, Wagner Jou; Barreto, Enoch Quinderé Sá; Barbosa, Maurício Mendes; Nardozza, Luciano Marcondes; Moron, Antonio Fernandes

    2015-01-01

    Congenital central nervous system tumors diagnosed during pregnancy are rare, and often have a poor prognosis. The most frequent type is the teratoma. Use of ultrasound and magnetic resonance image allows the suspicion of brain tumors during pregnancy. However, the definitive diagnosis is only confirmed after birth by histology. The purpose of this mini-review article is to describe the general clinical aspects of intracranial tumors and describe the main fetal brain tumors. PMID:25628801

  13. Fetal brain tumors: Prenatal diagnosis by ultrasound and magnetic resonance imaging.

    PubMed

    Milani, Hérbene José; Araujo Júnior, Edward; Cavalheiro, Sérgio; Oliveira, Patrícia Soares; Hisaba, Wagner Jou; Barreto, Enoch Quinderé Sá; Barbosa, Maurício Mendes; Nardozza, Luciano Marcondes; Moron, Antonio Fernandes

    2015-01-28

    Congenital central nervous system tumors diagnosed during pregnancy are rare, and often have a poor prognosis. The most frequent type is the teratoma. Use of ultrasound and magnetic resonance image allows the suspicion of brain tumors during pregnancy. However, the definitive diagnosis is only confirmed after birth by histology. The purpose of this mini-review article is to describe the general clinical aspects of intracranial tumors and describe the main fetal brain tumors. PMID:25628801

  14. Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

    2014-03-01

    Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

  15. Fetal autonomic brain age scores, segmented heart rate variability analysis, and traditional short term variability.

    PubMed

    Hoyer, Dirk; Kowalski, Eva-Maria; Schmidt, Alexander; Tetschke, Florian; Nowack, Samuel; Rudolph, Anja; Wallwitz, Ulrike; Kynass, Isabelle; Bode, Franziska; Tegtmeyer, Janine; Kumm, Kathrin; Moraru, Liviu; Götz, Theresa; Haueisen, Jens; Witte, Otto W; Schleußner, Ekkehard; Schneider, Uwe

    2014-01-01

    Disturbances of fetal autonomic brain development can be evaluated from fetal heart rate patterns (HRP) reflecting the activity of the autonomic nervous system. Although HRP analysis from cardiotocographic (CTG) recordings is established for fetal surveillance, temporal resolution is low. Fetal magnetocardiography (MCG), however, provides stable continuous recordings at a higher temporal resolution combined with a more precise heart rate variability (HRV) analysis. A direct comparison of CTG and MCG based HRV analysis is pending. The aims of the present study are: (i) to compare the fetal maturation age predicting value of the MCG based fetal Autonomic Brain Age Score (fABAS) approach with that of CTG based Dawes-Redman methodology; and (ii) to elaborate fABAS methodology by segmentation according to fetal behavioral states and HRP. We investigated MCG recordings from 418 normal fetuses, aged between 21 and 40 weeks of gestation. In linear regression models we obtained an age predicting value of CTG compatible short term variability (STV) of R (2) = 0.200 (coefficient of determination) in contrast to MCG/fABAS related multivariate models with R (2) = 0.648 in 30 min recordings, R (2) = 0.610 in active sleep segments of 10 min, and R (2) = 0.626 in quiet sleep segments of 10 min. Additionally segmented analysis under particular exclusion of accelerations (AC) and decelerations (DC) in quiet sleep resulted in a novel multivariate model with R (2) = 0.706. According to our results, fMCG based fABAS may provide a promising tool for the estimation of fetal autonomic brain age. Beside other traditional and novel HRV indices as possible indicators of developmental disturbances, the establishment of a fABAS score normogram may represent a specific reference. The present results are intended to contribute to further exploration and validation using independent data sets and multicenter research structures. PMID:25505399

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

    ClinicalTrials.gov

    2016-07-08

    Brain Injury; Central Nervous System Degenerative Disorder; Central Nervous System Infectious Disorder; Central Nervous System Vascular Malformation; Hemorrhagic Cerebrovascular Accident; Ischemic Cerebrovascular Accident; Primary Brain Neoplasm; Brain Cancer; Brain Tumors

  17. Gliomatosis cerebri: no evidence for a separate brain tumor entity.

    PubMed

    Herrlinger, Ulrich; Jones, David T W; Glas, Martin; Hattingen, Elke; Gramatzki, Dorothee; Stuplich, Moritz; Felsberg, Jörg; Bähr, Oliver; Gielen, Gerrit H; Simon, Matthias; Wiewrodt, Dorothee; Schabet, Martin; Hovestadt, Volker; Capper, David; Steinbach, Joachim P; von Deimling, Andreas; Lichter, Peter; Pfister, Stefan M; Weller, Michael; Reifenberger, Guido

    2016-02-01

    Gliomatosis cerebri (GC) is presently considered a distinct astrocytic glioma entity according to the WHO classification for CNS tumors. It is characterized by widespread, typically bilateral infiltration of the brain involving three or more lobes. Genetic studies of GC have to date been restricted to the analysis of individual glioma-associated genes, which revealed mutations in the isocitrate dehydrogenase 1 (IDH1) and tumor protein p53 (TP53) genes in subsets of patients. Here, we report on a genome-wide analysis of DNA methylation and copy number aberrations in 25 GC patients. Results were compared with those obtained for 105 patients with various types of conventional, i.e., non-GC gliomas including diffuse astrocytic gliomas, oligodendrogliomas and glioblastomas. In addition, we assessed the prognostic role of methylation profiles and recurrent DNA copy number aberrations in GC patients. Our data reveal that the methylation profiles in 23 of the 25 GC tumors corresponded to either IDH mutant astrocytoma (n = 6), IDH mutant and 1p/19q codeleted oligodendroglioma (n = 5), or IDH wild-type glioblastoma including various molecular subgroups, i.e., H3F3A-G34 mutant (n = 1), receptor tyrosine kinase 1 (RTK1, n = 4), receptor tyrosine kinase 2 (classic) (RTK2, n = 2) or mesenchymal (n = 5) glioblastoma groups. Two tumors showed methylation profiles of normal brain tissue due to low tumor cell content. While histological grading (WHO grade IV vs. WHO grade II and III) was not prognostic, the molecular classification as classic/RTK2 or mesenchymal glioblastoma was associated with worse overall survival. Multivariate Cox regression analysis revealed MGMT promoter methylation as a positive prognostic factor. Taken together, DNA-based large-scale molecular profiling indicates that GC comprises a genetically and epigenetically heterogeneous group of diffuse gliomas that carry DNA methylation and copy number profiles closely matching the common molecularly

  18. Intraoperative brain tumor resection cavity characterization with conoscopic holography

    NASA Astrophysics Data System (ADS)

    Simpson, Amber L.; Burgner, Jessica; Chen, Ishita; Pheiffer, Thomas S.; Sun, Kay; Thompson, Reid C.; Webster, Robert J., III; Miga, Michael I.

    2012-02-01

    Brain shift compromises the accuracy of neurosurgical image-guided interventions if not corrected by either intraoperative imaging or computational modeling. The latter requires intraoperative sparse measurements for constraining and driving model-based compensation strategies. Conoscopic holography, an interferometric technique that measures the distance of a laser light illuminated surface point from a fixed laser source, was recently proposed for non-contact surface data acquisition in image-guided surgery and is used here for validation of our modeling strategies. In this contribution, we use this inexpensive, hand-held conoscopic holography device for intraoperative validation of our computational modeling approach to correcting for brain shift. Laser range scan, instrument swabbing, and conoscopic holography data sets were collected from two patients undergoing brain tumor resection therapy at Vanderbilt University Medical Center. The results of our study indicate that conoscopic holography is a promising method for surface acquisition since it requires no contact with delicate tissues and can characterize the extents of structures within confined spaces. We demonstrate that for two clinical cases, the acquired conoprobe points align with our model-updated images better than the uncorrected images lending further evidence that computational modeling approaches improve the accuracy of image-guided surgical interventions in the presence of soft tissue deformations.

  19. The Role of Fast Cell Cycle Analysis in Pediatric Brain Tumors.

    PubMed

    Alexiou, George A; Vartholomatos, George; Stefanaki, Kalliopi; Lykoudis, Efstathios G; Patereli, Amalia; Tseka, Georgia; Tzoufi, Meropi; Sfakianos, George; Prodromou, Neofytos

    2015-01-01

    Cell cycle analysis by flow cytometry has not been adequately studied in pediatric brain tumors. We investigated the value of a modified rapid (within 6 min) cell cycle analysis protocol for the characterization of malignancy of pediatric brain tumors and for the differentiation of neoplastic from nonneoplastic tissue for possible intraoperative application. We retrospectively studied brain tumor specimens from patients treated at our institute over a 5-year period. All tumor samples were histopathologically verified before flow-cytometric analysis. The histopathological examination of permanent tissue sections was the gold standard. There were 68 brain tumor cases. All tumors had significantly lower G0/G1 and significantly higher S phase and mitosis fractions than normal brain tissue. Furthermore low-grade tumors could be differentiated from high-grade tumors. DNA aneuploidy was detected in 35 tumors. A correlation between S phase fraction and Ki-67 index was found in medulloblastomas and anaplastic ependymomas. Rapid cell cycle analysis by flow cytometry is a promising method for the identification of neoplastic tissue intraoperatively. Low-grade tumors could be differentiated from high-grade tumors. Thus, cell cycle analysis can be a valuable adjunct to the histopathological evaluation of pediatric brain tumors, whereas its intraoperative application warrants further investigation. PMID:26287721

  20. Subvoxel segmentation and representation of brain cortex using fuzzy clustering and gradient vector diffusion

    NASA Astrophysics Data System (ADS)

    Chang, Ming-Ching; Tao, Xiaodong

    2010-03-01

    Segmentation and representation of human brain cortex from Magnetic Resonance (MR) images is an important step for visualization and analysis in many neuro imaging applications. In this paper, we propose an automatic and fast algorithm to segment the brain cortex and to represent it as a geometric surface on which analysis can be carried out. The algorithm works on T1 weighted MR brain images with extracranial tissue removed. A fuzzy clustering algorithm with a parametric bias field model is applied to assign membership values of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) to each voxel. The cortical boundaries, namely the WM-GM and GM-CSF boundary surfaces, are extracted as iso-surfaces of functions derived from these membership functions. The central surface (CS), which traces the peak values (or ridges) of the GM membership function, is then extracted using gradient vector diffusion. Our main contribution is to provide a generic, accurate, fast, yet fully-automatic approach to (i) produce a soft segmentation of the MR brain image with intensity field correction, (ii) extract both the boundary and the center of the cortex in a surface form, where the topology and geometry can be explicitly examined, and (iii) use the extracted surfaces to model the curvy, folding cortical volume, which allows an intuitive measurement of the thickness. As a demonstration, we compute cortical thickness from the surfaces and compare the results with what has been reported in the literature. The entire process from raw MR image to cortical surface reconstruction takes on average between five to ten minutes.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  2. Brain Magnetic Resonance Imaging After High-Dose Chemotherapy and Radiotherapy for Childhood Brain Tumors

    SciTech Connect

    Spreafico, Filippo Gandola, Lorenza; Marchiano, Alfonso; Simonetti, Fabio; Poggi, Geraldina; Adduci, Anna; Clerici, Carlo Alfredo; Luksch, Roberto; Biassoni, Veronica; Meazza, Cristina; Catania, Serena; Terenziani, Monica; Musumeci, Renato; Fossati-Bellani, Franca; Massimino, Maura

    2008-03-15

    Purpose: Brain necrosis or other subacute iatrogenic reactions has been recognized as a potential complication of radiotherapy (RT), although the possible synergistic effects of high-dose chemotherapy and RT might have been underestimated. Methods and Materials: We reviewed the clinical and radiologic data of 49 consecutive children with malignant brain tumors treated with high-dose thiotepa and autologous hematopoietic stem cell rescue, preceded or followed by RT. The patients were assessed for neurocognitive tests to identify any correlation with magnetic resonance imaging (MRI) anomalies. Results: Of the 49 children, 18 (6 of 25 with high-grade gliomas and 12 of 24 with primitive neuroectodermal tumors) had abnormal brain MRI findings occurring a median of 8 months (range, 2-39 months) after RT and beginning to regress a median of 13 months (range, 2-26 months) after onset. The most common lesion pattern involved multiple pseudonodular, millimeter-size, T{sub 1}-weighted unevenly enhancing, and T{sub 2}-weighted hyperintense foci. Four patients with primitive neuroectodermal tumors also had subdural fluid leaks, with meningeal enhancement over the effusion. One-half of the patients had symptoms relating to the new radiographic findings. The MRI lesion-free survival rate was 74% {+-} 6% at 1 year and 57% {+-} 8% at 2 years. The number of marrow ablative courses correlated significantly to the incidence of radiographic anomalies. No significant difference was found in intelligent quotient scores between children with and without radiographic changes. Conclusion: Multiple enhancing cerebral lesions were frequently seen on MRI scans soon after high-dose chemotherapy and RT. Such findings pose a major diagnostic challenge in terms of their differential diagnosis vis-a-vis recurrent tumor. Their correlation with neurocognitive results deserves further investigation.

  3. An effective method for segmentation of MR brain images using the ant colony optimization algorithm.

    PubMed

    Taherdangkoo, Mohammad; Bagheri, Mohammad Hadi; Yazdi, Mehran; Andriole, Katherine P

    2013-12-01

    Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area. PMID:23563793

  4. Intra-operative brain tumor detection using elastic light single-scattering spectroscopy: a feasibility study

    NASA Astrophysics Data System (ADS)

    Canpolat, Murat; Akyüz, Mahmut; Gökhan, Güzide Ayşe; Gürer, Elif Inanç; Tuncer, Recai

    2009-09-01

    We have investigated the potential application of elastic light single-scattering spectroscopy (ELSSS) as an adjunctive tool for intraoperative rapid detection of brain tumors and demarcation of the tumor from the surrounding normal tissue. Measurements were performed on 29 excised tumor specimens from 29 patients. There were 21 instances of low-grade tumors and eight instances of high-grade tumors. Normal gray matter and white matter brain tissue specimens of four epilepsy patients were used as a control group. One low-grade and one high-grade tumor were misclassified as normal brain tissue. Of the low- and high-grade tumors, 20 out of 21 and 7 out of 8 were correctly classified by the ELSSS system, respectively. One normal white matter tissue margin was detected in a high-grade tumor, and three normal tissue margins were detected in three low-grade tumors using spectroscopic data analysis and confirmed by histopathology. The spectral slopes were shown to be positive for normal white matter brain tissue and negative for normal gray matter and tumor tissues. Our results indicate that signs of spectral slopes may enable the discrimination of brain tumors from surrounding normal white matter brain tissue with a sensitivity of 93% and specificity of 100%.

  5. Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

    NASA Astrophysics Data System (ADS)

    Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

    2012-01-01

    The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

  6. Using 3-D shape models to guide segmentation of MR brain images.

    PubMed Central

    Hinshaw, K. P.; Brinkley, J. F.

    1997-01-01

    Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters. Images Figure 1 Figure 2 Figure 3 Figure 4 PMID:9357670

  7. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

    PubMed

    Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  8. Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

    PubMed Central

    Yang, Zhang; Li, Guo; Weifeng, Ding

    2016-01-01

    The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

  9. Magnetic resonance imaging diagnosis of brain tumors in dogs.

    PubMed

    Bentley, R Timothy

    2015-08-01

    A great deal of information is now available regarding the range of magnetic resonance imaging (MRI) features of many primary and secondary brain tumors from dogs. In this review, these canine neoplasms are grouped into meningeal masses, ventricular masses, intra-axial enhancing lesions, intra-axial mildly to non-enhancing lesions, and multifocal lesions. For each of these patterns, the major and sporadic neoplastic differential diagnoses are provided, and guidance on how to rank differential diagnoses for each individual patient is presented. The implication of MRI features such as contrast-enhancement, signal intensities and location is discussed. However, the information garnered from MRI must be correlated with all available clinical information and with epidemiological data before creating a differential diagnosis. PMID:25792181

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

    PubMed

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

    2015-01-01

    The Brain Tumor Epidemiology Consortium (BTEC) is an open scientific forum, which fosters the development of multi-center, international and inter-disciplinary collaborations. BTEC aims to develop a better understanding of the etiology, outcomes, and prevention of brain tumors (http://epi.grants.cancer.gov/btec/). The 15th annual Brain Tumor Epidemiology Consortium Meeting, hosted by the Austrian Societies of Neuropathology and Neuro-oncology, was held on September 9 - 11, 2014 in Vienna, Austria. The meeting focused on the central role of brain tumor epidemiology within multidisciplinary neuro-oncology. Knowledge of disease incidence, outcomes, as well as risk factors is fundamental to all fields involved in research and treatment of patients with brain tumors; thus, epidemiology constitutes an important link between disciplines, indeed the very hub. This was reflected by the scientific program, which included various sessions linking brain tumor epidemiology with clinical neuro-oncology, tissue-based research, and cancer registration. Renowned experts from Europe and the United States contributed their personal perspectives stimulating further group discussions. Several concrete action plans evolved for the group to move forward until next year's meeting, which will be held at the Mayo Clinic at Rochester, MN, USA. PMID:25518914

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

    NASA Astrophysics Data System (ADS)

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

    2009-11-01

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

  12. WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

    SciTech Connect

    Gensheimer, M; Trister, A; Ermoian, R; Hawkins, D

    2014-06-15

    Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was

  13. The expression of BST2 in human and experimental mouse brain tumors

    PubMed Central

    Wainwright, Derek A.; Balyasnikova, Irina V.; Han, Yu; Lesniak, Maciej S.

    2011-01-01

    Glioblastoma multiforme (grade IV astrocytoma) is a highly malignant brain tumor with poor treatment options and an average lifespan of 15 months after diagnosis. Previous work has demonstrated that BST2 (bone marrow stromal cell antigen 2; also known as PDCA-1, CD137 and HM1.24) is expressed by multiple myeloma, endometrial cancer and primary lung cancer cells. BST2 is expressed on the plasma membrane, which makes it an ideal target for immunotherapy. Accordingly, several groups have shown BST2 mAb to be effective for targeting tumor cells. In this report, we hypothesized that BST2 is expressed in human and mouse brain tumors and plays a critical role in brain tumor progression. We show that BST2 mRNA expression is increased in mouse brain IC-injected with GL261 cells, when compared to mouse brain IC-injected with saline at 3 weeks post-operative (p < 0.05). To test the relevance of BST2, we utilized the intracranially (IC)-injected GL261 cell-based malignant brain tumor mouse model. We show that BST2 mRNA expression is increased in mouse brain IC-injected GL261 cells, when compared to mouse brain IC-injected saline at 3 weeks post-operative (p < 0.05). Furthermore, BST2 immunofluorescence predominantly localized to mouse brain tumor cells. Finally, mice IC-injected with GL261 cells transduced with shRNA for BST2 ± pre-incubation with BST2 mAb show no difference in overall lifespan when compared to mice IC-injected with GL261 cells transduced with a scrambled shRNA ± pre-incubation with BST2 mAb. Collectively, these data show that while BST2 expression increases during brain tumor progression in both human and mouse brain tumors, it has no apparent consequences to overall lifespan in an orthotopic mouse brain tumor model. PMID:21565182

  14. Significant predictors of patients' uncertainty in primary brain tumors.

    PubMed

    Lin, Lin; Chien, Lung-Chang; Acquaye, Alvina A; Vera-Bolanos, Elizabeth; Gilbert, Mark R; Armstrong, Terri S

    2015-05-01

    Patients with primary brain tumors (PBT) face uncertainty related to prognosis, symptoms and treatment response and toxicity. Uncertainty is correlated to negative mood states and symptom severity and interference. This study identified predictors of uncertainty during different treatment stages (newly-diagnosed, on treatment, followed-up without active treatment). One hundred eighty six patients with PBT were accrued at various points in the illness trajectory. Data collection tools included: a clinical checklist/a demographic data sheet/the Mishel Uncertainty in Illness Scale-Brain Tumor Form. The structured additive regression model was used to identify significant demographic and clinical predictors of illness-related uncertainty. Participants were primarily white (80 %) males (53 %). They ranged in age from 19-80 (mean = 44.2 ± 12.6). Thirty-two of the 186 patients were newly-diagnosed, 64 were on treatment at the time of clinical visit with MRI evaluation, 21 were without MRI, and 69 were not on active treatment. Three subscales (ambiguity/inconsistency; unpredictability-disease prognoses; unpredictability-symptoms and other triggers) were different amongst the treatment groups (P < .01). However, patients' uncertainty during active treatment was as high as in newly-diagnosed period. Other than treatment stages, change of employment status due to the illness was the most significant predictor of illness-related uncertainty. The illness trajectory of PBT remains ambiguous, complex, and unpredictable, leading to a high incidence of uncertainty. There was variation in the subscales of uncertainty depending on treatment status. Although patients who are newly diagnosed reported the highest scores on most of the subscales, patients on treatment felt more uncertain about unpredictability of symptoms than other groups. Due to the complexity and impact of the disease, associated symptoms, and interference with functional status, comprehensive assessment of patients

  15. Household pesticides and risk of pediatric brain tumors.

    PubMed Central

    Pogoda, J M; Preston-Martin, S

    1997-01-01

    A follow-up to a population-based case-control study of pediatric brain tumors in Los Angeles County, California, involving mothers of 224 cases and 218 controls, investigated the risk of household pesticide use from pregnancy to diagnosis. Risk was significantly elevated for prenatal exposure to flea/tick pesticides -odds ratio (OR) = 1.7; 95% confidence interval (CI), 1.1-2.6-, particularly among subjects less than 5 years old at diagnosis (OR = 2.5; CI, 1. 2-5.5). Prenatal risk was highest for mothers who prepared, applied, or cleaned up flea/tick products themselves (OR = 2.2; CI, 1.1-4.2; for subjects <5 years of age, OR = 5.4; CI, 1.3-22.3). A significant trend of increased risk with increased exposure was observed for number of pets treated (p = 0.04). Multivariate analysis of types of flea/tick products indicated that sprays/foggers were the only products significantly related to risk (OR =10.8; CI, 1.3-89.1). Elevated risks were not observed for termite or lice treatments, pesticides for nuisance pests, or yard and garden insecticides, herbicides, fungicides, or snail killer. Certain precautions,if ignored, were associated with significant increased risk: evacuating the house after spraying or dusting for pests (OR = 1.6; CI, 1.0-2.6), delaying the harvest of food after pesticide treatment (OR = 3.6; CI, 1.0-13.7), and following instructions on pesticide labels (OR = 3. 7;CI, 1.5-9.6). These findings indicate that chemicals used in flea/tick products may increase risk of pediatric brain tumors and suggest that further research be done to pinpoint specific chemicals involved. PMID:9370522

  16. Integration of Sparse Multi-modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation

    PubMed Central

    Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

    2014-01-01

    Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination process. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8 months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6 months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter. PMID:24291615

  17. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks.

    PubMed

    Reddick, W E; Glass, J O; Cook, E N; Elkin, T D; Deaton, R J

    1997-12-01

    We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; seven male, seven female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (ri) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p < 0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability. PMID:9533591

  18. New therapeutic approach for brain tumors: Intranasal delivery of telomerase inhibitor GRN163

    PubMed Central

    Hashizume, Rintaro; Ozawa, Tomoko; Gryaznov, Sergei M.; Bollen, Andrew W.; Lamborn, Kathleen R.; Frey, William H.; Deen, Dennis F.

    2008-01-01

    The blood-brain barrier is a substantial obstacle for delivering anticancer agents to brain tumors, and new strategies for bypassing it are greatly needed for brain-tumor therapy. Intranasal delivery provides a practical, noninvasive method for delivering therapeutic agents to the brain and could provide an alternative to intravenous injection and convection-enhanced delivery. We treated rats bearing intracerebral human tumor xeno-grafts intranasally with GRN163, an oligonucleotide N3′→P5′thio-phosphoramidate telomerase inhibitor. 3′-Fuorescein isothiocyanate (FITC)–labeled GRN163 was administered intranasally every 2 min as 6 μl drops into alternating sides of the nasal cavity over 22 min. FITC-labeled GRN163 was present in tumor cells at all time points studied, and accumulation of GRN163 peaked at 4 h after delivery. Moreover, GRN163 delivered intranasally, daily for 12 days, significantly prolonged the median survival from 35 days in the control group to 75.5 days in the GRN163-treated group. Thus, intranasal delivery of GRN163 readily bypassed the blood-brain barrier, exhibited favorable tumor uptake, and inhibited tumor growth, leading to a prolonged lifespan for treated rats compared to controls. This delivery approach appears to kill tumor cells selectively, and no toxic effects were noted in normal brain tissue. These data support further development of intranasal delivery of tumor-specific therapeutic agents for brain tumor patients. PMID:18287341

  19. New therapeutic approach for brain tumors: Intranasal delivery of telomerase inhibitor GRN163.

    PubMed

    Hashizume, Rintaro; Ozawa, Tomoko; Gryaznov, Sergei M; Bollen, Andrew W; Lamborn, Kathleen R; Frey, William H; Deen, Dennis F

    2008-04-01

    The blood-brain barrier is a substantial obstacle for delivering anticancer agents to brain tumors, and new strategies for bypassing it are greatly needed for brain-tumor therapy. Intranasal delivery provides a practical, noninvasive method for delivering therapeutic agents to the brain and could provide an alternative to intravenous injection and convection-enhanced delivery. We treated rats bearing intracerebral human tumor xenografts intranasally with GRN163, an oligonucleotide N3'-->P5'thio-phosphoramidate telomerase inhibitor. 3'-Fuorescein isothiocyanate (FITC)-labeled GRN163 was administered intranasally every 2 min as 6 microl drops into alternating sides of the nasal cavity over 22 min. FITC-labeled GRN163 was present in tumor cells at all time points studied, and accumulation of GRN163 peaked at 4 h after delivery. Moreover, GRN163 delivered intranasally, daily for 12 days, significantly prolonged the median survival from 35 days in the control group to 75.5 days in the GRN163-treated group. Thus, intranasal delivery of GRN163 readily bypassed the blood-brain barrier, exhibited favorable tumor uptake, and inhibited tumor growth, leading to a prolonged lifespan for treated rats compared to controls. This delivery approach appears to kill tumor cells selectively, and no toxic effects were noted in normal brain tissue. These data support further development of intranasal delivery of tumor-specific therapeutic agents for brain tumor patients. PMID:18287341

  20. Microtubule-associated protein tau in bovine retinal photoreceptor rod outer segments: comparison with brain tau.

    PubMed

    Yamazaki, Akio; Nishizawa, Yuji; Matsuura, Isao; Hayashi, Fumio; Usukura, Jiro; Bondarenko, Vladimir A

    2013-10-01

    Recent studies have suggested a possible involvement of abnormal tau in some retinal degenerative diseases. The common view in these studies is that these retinal diseases share the mechanism of tau-mediated degenerative diseases in brain and that information about these brain diseases may be directly applied to explain these retinal diseases. Here we collectively examine this view by revealing three basic characteristics of tau in the rod outer segment (ROS) of bovine retinal photoreceptors, i.e., its isoforms, its phosphorylation mode and its interaction with microtubules, and by comparing them with those of brain tau. We find that ROS contains at least four isoforms: three are identical to those in brain and one is unique in ROS. All ROS isoforms, like brain isoforms, are modified with multiple phosphate molecules; however, ROS isoforms show their own specific phosphorylation pattern, and these phosphorylation patterns appear not to be identical to those of brain tau. Interestingly, some ROS isoforms, under the normal conditions, are phosphorylated at the sites identical to those in Alzheimer's patient isoforms. Surprisingly, a large portion of ROS isoforms tightly associates with a membranous component(s) other than microtubules, and this association is independent of their phosphorylation states. These observations strongly suggest that tau plays various roles in ROS and that some of these functions may not be comparable to those of brain tau. We believe that knowledge about tau in the entire retinal network and/or its individual cells are also essential for elucidation of tau-mediated retinal diseases, if any. PMID:23712071

  1. Donepezil in Treating Young Patients With Primary Brain Tumors Previously Treated With Radiation Therapy to the Brain

    ClinicalTrials.gov

    2016-07-26

    Brain and Central Nervous System Tumors; Cognitive/Functional Effects; Long-term Effects Secondary to Cancer Therapy in Children; Neurotoxicity; Psychosocial Effects of Cancer and Its Treatment; Radiation Toxicity

  2. Imaging of tumor angiogenesis in rat brains in vivo by photoacoustic tomography

    NASA Astrophysics Data System (ADS)

    Ku, Geng; Wang, Xueding; Xie, Xueyi; Stoica, George; Wang, Lihong V.

    2005-02-01

    Green laser pulses at a wavelength of 532 nm from a Q-switched Nd:YAG laser were employed as irradiation sources for photoacoustic tomography (PAT). The vascular structure of the brain was imaged clearly, with optimal contrast, because blood has strong absorption near this wavelength. The photoacoustic images of rat brain tumors in this study clearly reveal the angiogenesis that is associated with tumors. Brain tumors can be identified based on the distorted vascular architecture of brain tumorigenesis and related vascular changes, such as hemorrhage. This research demonstrates that PAT can potentially provide a powerful tool for small-animal biological research.

  3. Statistical model of laminar structure for atlas-based segmentation of the fetal brain from in utero MR images

    NASA Astrophysics Data System (ADS)

    Habas, Piotr A.; Kim, Kio; Chandramohan, Dharshan; Rousseau, Francois; Glenn, Orit A.; Studholme, Colin

    2009-02-01

    Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain segmentation are limited in their ability to accurately delineate complex structures of developing tissues from fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient (DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to 0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.

  4. Collective behavior of brain tumor cells: The role of hypoxia

    NASA Astrophysics Data System (ADS)

    Khain, Evgeniy; Katakowski, Mark; Hopkins, Scott; Szalad, Alexandra; Zheng, Xuguang; Jiang, Feng; Chopp, Michael

    2011-03-01

    We consider emergent collective behavior of a multicellular biological system. Specifically, we investigate the role of hypoxia (lack of oxygen) in migration of brain tumor cells. We performed two series of cell migration experiments. In the first set of experiments, cell migration away from a tumor spheroid was investigated. The second set of experiments was performed in a typical wound-healing geometry: Cells were placed on a substrate, a scratch was made, and cell migration into the gap was investigated. Experiments show a surprising result: Cells under normal and hypoxic conditions have migrated the same distance in the “spheroid” experiment, while in the “scratch” experiment cells under normal conditions migrated much faster than under hypoxic conditions. To explain this paradox, we formulate a discrete stochastic model for cell dynamics. The theoretical model explains our experimental observations and suggests that hypoxia decreases both the motility of cells and the strength of cell-cell adhesion. The theoretical predictions were further verified in independent experiments.

  5. Occupational exposure to electromagnetic fields and the occurrence of brain tumors. An analysis of possible associations.

    PubMed

    Lin, R S; Dischinger, P C; Conde, J; Farrell, K P

    1985-06-01

    To explore the association between occupation and the occurrence of brain tumor, an epidemiologic study was conducted using data from the death certificates of 951 adult white male Maryland residents who died of brain tumor during the period 1969 through 1982. Compared with the controls, men employed in electricity-related occupations, such as electrician, electric or electronic engineer, and utility company serviceman, were found to experience a significantly higher proportion of primary brain tumors. An increase in the odds ratio for brain tumor was found to be positively related to electromagnetic (EM) field exposure levels. Furthermore, the mean age at death was found to be significantly younger among cases in the presumed high EM-exposure group. These findings suggest that EM exposure may be associated with the pathogenesis of brain tumors, particularly in the promoting stage. PMID:4020499

  6. Brain Tumor Initiating Cells Adapt to Restricted Nutrition through Preferential Glucose Uptake

    PubMed Central

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

    2013-01-01

    Like all cancers, brain tumors require a continuous source of energy and molecular resources for new cell production. In normal brain, glucose is an essential neuronal fuel, but the blood-brain barrier limits its delivery. We now report that nutrient restriction contributes to tumor progression by enriching for brain tumor initiating cells (BTICs) due to preferential BTIC survival and adaptation of non-BTICs through acquisition of BTIC features. BTICs outcompete for glucose uptake by co-opting the high affinity neuronal glucose transporter, type 3 (Glut3, SLC2A3). BTICs preferentially express Glut3 and targeting Glut3 inhibits BTIC growth and tumorigenic potential. Glut3, but not Glut1, correlates with poor survival in brain tumors and other cancers; thus, TICs may extract nutrients with high affinity. As altered metabolism represents a cancer hallmark, metabolic reprogramming may instruct the tumor hierarchy and portend poor prognosis. PMID:23995067

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

    PubMed Central

    Chaudhuri, Swapna

    2015-01-01

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

  8. Towards the use of HIFU, in Conjunction with Surgery, in the Treatment of Malignant Brain Tumors

    NASA Astrophysics Data System (ADS)

    Dahl, Elizabeth; Nguyen, Lisa T.; Sparks, Rachel E.; Brayman, Andy A.; Olios, Ryan J.; Silbergeld, Daniel L.; Vaezy, Sarah; Mourad, Pierre D.

    2006-05-01

    The first medical response to the presence of a brain tumor is often its resection, both to alleviate mass effect, and to obtain tissue for diagnosis, itself necessary for guiding adjunctive therapy. Malignant brain tumors typically recur at the tumor resection margin. Most current chemotherapy and radiotherapy strategies target local recurrence with limited success. Here we review a new strategy for delivering chemotherapeutics for brain tumor recurrence. It uses intra-operative high-intensity focused ultrasound (HIFU) to transiently open the blood-brain barrier (BBB) over a significantly large volume of brain at and near the resection margin to enhance the subsequent delivery of systemically delivered chemotherapeutic agents into the region of tumor recurrence.

  9. Calcium Channels and Associated Receptors in Malignant Brain Tumor Therapy.

    PubMed

    Morrone, Fernanda B; Gehring, Marina P; Nicoletti, Natália F

    2016-09-01

    Malignant brain tumors are highly lethal and aggressive. Despite recent advances in the current therapies, which include the combination of surgery and radio/chemotherapy, the average survival rate remains poor. Altered regulation of ion channels is part of the neoplastic transformation, which suggests that ion channels are involved in cancer. Distinct classes of calcium-permeable channels are abnormally expressed in cancer and are likely involved in the alterations underlying malignant growth. Specifically, cytosolic Ca(2+) activity plays an important role in the regulation of cell proliferation, and Ca(2+) signaling is altered in proliferating tumor cells. A series of previous studies emphasized the importance of the T-type low-voltage-gated calcium channels (VGCC) in different cancer types, including gliomas, and remarkably, pharmacologic inhibition of T-type VGCC caused antiproliferative effects and triggered apoptosis of human glioma cells. Other calcium permeable channels, such as transient receptor potential (TRP) channels, contribute to changes in Ca(2+) by modulating the driving force for Ca(2+) entry, and some TRP channels are required for proliferation and migration in gliomas. Furthermore, recent evidence shows that TRP channels contribute to the progression and survival of the glioblastoma patients. Likewise, the purinergic P2X7 receptor acts as a direct conduit for Ca(2+)-influx and an indirect activator of voltage-gated Ca(2+)-channel. Evidence also shows that P2X7 receptor activation is linked to elevated expression of inflammation promoting factors, tumor cell migration, an increase in intracellular mobilization of Ca(2+), and membrane depolarization in gliomas. Therefore, this review summarizes the recent findings on calcium channels and associated receptors as potential targets to treat malignant gliomas. PMID:27418672

  10. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data

    PubMed Central

    Niedworok, Christian J.; Brown, Alexander P. Y.; Jorge Cardoso, M.; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W.

    2016-01-01

    The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

  11. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data.

    PubMed

    Niedworok, Christian J; Brown, Alexander P Y; Jorge Cardoso, M; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W

    2016-01-01

    The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

  12. A Unified Framework for Cross-modality Multi-atlas Segmentation of Brain MRI

    PubMed Central

    Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van

    2013-01-01

    Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single final segmentation. Such label fusion schemes commonly rely on the similarity between intensity values of the atlases and target scan, which is often problematic in medical imaging - in particular, when the atlases and target images are obtained via different sensor types or imaging protocols. In this paper, we present a generative probabilistic model that yields an algorithm for solving the atlas-to-target registrations and label fusion steps simultaneously. The proposed model does not directly rely on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing interdependence between the registrations. We use variational expectation maximization and the Demons registration framework in order to efficiently identify the most probable segmentation and registrations. We use two sets of experiments to illustrate the approach, where proton density (PD) MRI atlases are used to segment T1-weighted brain scans and vice versa. Our results clearly demonstrate the accuracy gain due to exploiting within-target intensity consistency and integrating registration into label fusion. PMID:24001931

  13. A unified framework for cross-modality multi-atlas segmentation of brain MRI.

    PubMed

    Eugenio Iglesias, Juan; Rory Sabuncu, Mert; Van Leemput, Koen

    2013-12-01

    Multi-atlas label fusion is a powerful image segmentation strategy that is becoming increasingly popular in medical imaging. A standard label fusion algorithm relies on independently computed pairwise registrations between individual atlases and the (target) image to be segmented. These registrations are then used to propagate the atlas labels to the target space and fuse them into a single final segmentation. Such label fusion schemes commonly rely on the similarity between intensity values of the atlases and target scan, which is often problematic in medical imaging - in particular, when the atlases and target images are obtained via different sensor types or imaging protocols. In this paper, we present a generative probabilistic model that yields an algorithm for solving the atlas-to-target registrations and label fusion steps simultaneously. The proposed model does not directly rely on the similarity of image intensities. Instead, it exploits the consistency of voxel intensities within the target scan to drive the registration and label fusion, hence the atlases and target image can be of different modalities. Furthermore, the framework models the joint warp of all the atlases, introducing interdependence between the registrations. We use variational expectation maximization and the Demons registration framework in order to efficiently identify the most probable segmentation and registrations. We use two sets of experiments to illustrate the approach, where proton density (PD) MRI atlases are used to segment T1-weighted brain scans and vice versa. Our results clearly demonstrate the accuracy gain due to exploiting within-target intensity consistency and integrating registration into label fusion. PMID:24001931

  14. Elucidating the mechanobiology of malignant brain tumors using a brain matrix-mimetic hyaluronic acid hydrogel platform

    PubMed Central

    Ananthanarayanan, Badriprasad; Kim, Yushan; Kumar, Sanjay

    2011-01-01

    Glioblastoma multiforme (GBM) is a malignant brain tumor characterized by diffuse infiltration of single cells into the brain parenchyma, which is a process that relies in part on aberrant biochemical and biophysical interactions between tumor cells and the brain extracellular matrix (ECM). A major obstacle to understanding ECM regulation of GBM invasion is the absence of model matrix systems that recapitulate the distinct composition and physical structure of brain ECM while allowing independent control of adhesive ligand density, mechanics, and microstructure. To address this need, we synthesized brain-mimetic ECMs based on hyaluronic acid (HA) with a range of stiffnesses that encompasses normal and tumorigenic brain tissue and functionalized these materials with short Arg-Gly-Asp (RGD) peptides to facilitate cell adhesion. Scanning electron micrographs of the hydrogels revealed a dense, sheet-like microstructure with apparent nanoscale porosity similar to brain extracellular space. On flat hydrogel substrates, glioma cell spreading area and actin stress fiber assembly increased strongly with increasing density of RGD peptide. Increasing HA stiffness under constant RGD density produced similar trends and increased the speed of random motility. In a three-dimensional (3D) spheroid paradigm, glioma cells invaded HA hydrogels with morphological patterns distinct from those observed on flat surfaces or in 3D collagen-based ECMs but highly reminiscent of those seen in brain slices. This material system represents a brain-mimetic model ECM with tunable ligand density and stiffness amenable to investigations of the mechanobiological regulation of brain tumor progression. PMID:21820737

  15. Multi-Object Model-based Multi-Atlas Segmentation for Rodent Brains using Dense Discrete Correspondences

    PubMed Central

    Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

    2016-01-01

    The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra-subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool. PMID:27065200

  16. Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

    NASA Astrophysics Data System (ADS)

    Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

    2016-03-01

    The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

  17. Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images.

    PubMed

    Lundervold, A; Storvik, G

    1995-01-01

    Presents a new method to segment brain parenchyma and cerebrospinal fluid spaces automatically in routine axial spin echo multispectral MR images. The algorithm simultaneously incorporates information about anatomical boundaries (shape) and tissue signature (grey scale) using a priori knowledge. The head and brain are divided into four regions and seven different tissue types. Each tissue type c is modeled by a multivariate Gaussian distribution N(mu(c),Sigma(c)). Each region is associated with a finite mixture density corresponding to its constituent tissue types. Initial estimates of tissue parameters {mu(c),Sigma(c )}(c=1,...,7) are obtained from k-means clustering of a single slice used for training. The first algorithmic step uses the EM-algorithm for adjusting the initial tissue parameter estimates to the MR data of new patients. The second step uses a recently developed model of dynamic contours to detect three simply closed nonintersecting curves in the plane, constituting the arachnoid/dura mater boundary of the brain, the border between the subarachnoid space and brain parenchyma, and the inner border of the parenchyma toward the lateral ventricles. The model, which is formulated by energy functions in a Bayesian framework, incorporates a priori knowledge, smoothness constraints, and updated tissue type parameters. Satisfactory maximum a posteriori probability estimates of the closed contour curves defined by the model were found using simulated annealing. PMID:18215837

  18. Tumor

    MedlinePlus

    ... be removed because of their location or harmful effect on the surrounding normal brain tissue. If a tumor is cancer , possible treatments may include: Chemotherapy Radiation Surgery Targeted cancer therapy Biologic therapy Other treatment options

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

    PubMed Central

    Schutt, David J.; Haemmerich, Dieter

    2009-01-01

    Radiofrequency (RF) ablation has become an accepted treatment modality for unresectable tumors. The need for larger ablation zones has resulted in increased RF generator power. Skin burns due to ground pad heating are increasingly limiting further increases in generator power, and thus, ablation zone size. We investigated a method for reducing ground pad heating in which a commercial ground pad is segmented into multiple ground electrodes, with sequential activation of ground electrode subsets. We created finite-element method computer models of a commercial ground pad (14 × 23 cm) and compared normal operation of a standard pad to sequential activation of a segmented pad (two to five separate ground electrode segments). A constant current of 1 A was applied for 12 min in all simulations. Time periods during sequential activation simulations were adjusted to keep the leading edge temperatures at each ground electrode equal. The maximum temperature using standard activation of the commercial pad was 41.7 °C. For sequential activation of a segmented pad, the maximum temperature ranged from 39.3 °C (five segments) to 40.9 °C (two segments). Sequential activation of a segmented ground pad resulted in lower tissue temperatures. This method may reduce the incidence of ground pad burns and enable the use of higher power generators during RF tumor ablation. PMID:18595807

  20. Biphasic modeling of brain tumor biomechanics and response to radiation treatment

    PubMed Central

    Angeli, Stelios; Stylianopoulos, Triantafyllos

    2016-01-01

    Biomechanical forces are central in tumor progression and response to treatment. This becomes more important in brain cancers where tumors are surrounded by tissues with different mechanical properties. Existing mathematical models ignore direct mechanical interactions of the tumor with the normal brain. Here, we developed a clinically relevant model, which predicts tumor growth accounting directly for mechanical interactions. A three-dimensional model of the gray and white matter and the cerebrospinal fluid was constructed from magnetic resonance images of a normal brain. Subsequently, a biphasic tissue growth theory for an initial tumor seed was employed, incorporating the effects of radiotherapy. Additionally, three different sets of brain tissue properties taken from the literature were used to investigate their effect on tumor growth. Results show the evolution of solid stress and interstitial fluid pressure within the tumor and the normal brain. Heterogeneous distribution of the solid stress exerted on the tumor resulted in a 35 % spatial variation in cancer cell proliferation. Interestingly, the model predicted that distant from the tumor, normal tissues still undergo significant deformations while it was found that intratumoral fluid pressure is elevated. Our predictions relate to clinical symptoms of brain cancers and present useful tools for therapy planning. PMID:27086116

  1. Biphasic modeling of brain tumor biomechanics and response to radiation treatment.

    PubMed

    Angeli, Stelios; Stylianopoulos, Triantafyllos

    2016-06-14

    Biomechanical forces are central in tumor progression and response to treatment. This becomes more important in brain cancers where tumors are surrounded by tissues with different mechanical properties. Existing mathematical models ignore direct mechanical interactions of the tumor with the normal brain. Here, we developed a clinically relevant model, which predicts tumor growth accounting directly for mechanical interactions. A three-dimensional model of the gray and white matter and the cerebrospinal fluid was constructed from magnetic resonance images of a normal brain. Subsequently, a biphasic tissue growth theory for an initial tumor seed was employed, incorporating the effects of radiotherapy. Additionally, three different sets of brain tissue properties taken from the literature were used to investigate their effect on tumor growth. Results show the evolution of solid stress and interstitial fluid pressure within the tumor and the normal brain. Heterogeneous distribution of the solid stress exerted on the tumor resulted in a 35% spatial variation in cancer cell proliferation. Interestingly, the model predicted that distant from the tumor, normal tissues still undergo significant deformations while it was found that intratumoral fluid pressure is elevated. Our predictions relate to clinical symptoms of brain cancers and present useful tools for therapy planning. PMID:27086116

  2. Hierarchical brain tissue segmentation and its application in multiple sclerosis and Alzheimer's disease

    NASA Astrophysics Data System (ADS)

    Lei, Tianhu; Udupa, Jayaram K.; Moonis, Gul; Schwartz, Eric; Balcer, Laura

    2005-04-01

    Based on Fuzzy Connectedness (FC) object delineation principles and algorithms, a hierarchical brain tissue segmentation technique has been developed for MR images. After MR image background intensity inhomogeneity correction and intensity standardization, three FC objects for cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) are generated via FC object delineation, and an intracranial (IC) mask is created via morphological operations. Then, the IC mask is decomposed into parenchymal (BP) and CSF masks, while the BP mask is separated into WM and GM masks. WM mask is further divided into pure and dirty white matter masks (PWM and DWM). In Multiple Sclerosis studies, a severe white matter lesion (LS) mask is defined from DWM mask. Based on the segmented brain tissue images, a histogram-based method has been developed to find disease-specific, image-based quantitative markers for characterizing the macromolecular manifestation of the two diseases. These same procedures have been applied to 65 MS (46 patients and 19 normal subjects) and 25 AD (15 patients and 10 normal subjects) data sets, each of which consists of FSE PD- and T2-weighted MR images. Histograms representing standardized PD and T2 intensity distributions and their numerical parameters provide an effective means for characterizing the two diseases. The procedures are systematic, nearly automated, robust, and the results are reproducible.

  3. Brain Metastasis from Gastrointestinal Stromal Tumor: A Case Report and Review of the Literature

    PubMed Central

    Naoe, Hideaki; Kaku, Eisuke; Ido, Yumi; Gushima, Rika; Maki, Yoko; Saito, Hirokazu; Yokote, Seiichiro; Gushima, Ryosuke; Nonaka, Kouichi; Hoshida, Yohmei; Murao, Tetsuya; Ozaki, Tetsu; Yokomine, Kazunori; Tanaka, Hideki; Nagahama, Hiroyasu; Sakurai, Kouichi; Tanaka, Motohiko; Iyama, Ken-ichi; Baba, Hideo; Sasaki, Yutaka

    2011-01-01

    Metastasis of gastrointestinal stromal tumor (GIST) into the central nervous system is extremely rare. We report a patient with synchronous GIST and brain metastasis. At disease onset, there was left hemiplegia and ptosis of the right eyelids. Resection cytology of the brain tumor was reported as metastasis of GIST. After positron emission tomography examination, another tumor in the small bowel was discovered, which suggested a small bowel GIST associated with intracranial metastasis. Immunohistochemical analysis of the intestinal tumor specimen obtained by double balloon endoscopy showed a pattern similar to the brain tumor, with the tumors subsequently identified as intracranial metastases of jejunal GIST. After surgical resection of one brain tumor, the patient underwent whole brain radiation therapy followed by treatment with imatinib mesylate (Gleevec; Novartis Pharma, Basel, Switzerland). Mutational analysis of the original intestinal tumor revealed there were no gene alterations in KIT or PDGFRα. Since the results indicated the treatment had no apparent effect on either of the tumors, and because ileus developed due to an intestinal primary tumor, the patient underwent surgical resection of the intestinal lesion. However, the patient's condition gradually worsen and she subsequently died 4 months after the initial treatment. PMID:22110419

  4. The expression of BST2 in human and experimental mouse brain tumors.

    PubMed

    Wainwright, Derek A; Balyasnikova, Irina V; Han, Yu; Lesniak, Maciej S

    2011-08-01

    Glioblastoma multiforme (grade IV astrocytoma) is a highly malignant brain tumor with poor treatment options and an average lifespan of 15 months after diagnosis. Previous work has demonstrated that BST2 (bone marrow stromal cell antigen 2; also known as PDCA-1, CD137 and HM1.24) is expressed by multiple myeloma, endometrial cancer and primary lung cancer cells. BST2 is expressed on the plasma membrane, which makes it an ideal target for immunotherapy. Accordingly, several groups have shown BST2 mAb to be effective for targeting tumor cells. In this report, we hypothesized that BST2 is expressed in human and mouse brain tumors and plays a critical role in brain tumor progression. We show that BST2 expression is upregulated at both the mRNA and protein level in high grade when compared to low grade human astrocytoma (p<0.05). To test the relevance of BST2, we utilized the intracranially (IC)-injected GL261 cell-based malignant brain tumor mouse model. We show that BST2 mRNA expression is increased in mouse brain IC-injected with GL261 cells, when compared to mouse brain IC-injected with saline at 3 weeks post-operative (p<0.05). Furthermore, BST2 immunofluorescence predominantly localized to mouse brain tumor cells. Finally, mice IC-injected with GL261 cells transduced with shRNA for BST2±preincubated with BST2 mAb show no difference in overall lifespan when compared to mice IC-injected with GL261 cells transduced with a scrambled shRNA±preincubated with BST2 mAb. Collectively, these data show that while BST2 expression increases during brain tumor progression in both human and mouse brain tumors, it has no apparent consequences to overall lifespan in an orthotopic mouse brain tumor model. PMID:21565182

  5. Invited review--neuroimaging response assessment criteria for brain tumors in veterinary patients.

    PubMed

    Rossmeisl, John H; Garcia, Paulo A; Daniel, Gregory B; Bourland, John Daniel; Debinski, Waldemar; Dervisis, Nikolaos; Klahn, Shawna

    2014-01-01

    The evaluation of therapeutic response using cross-sectional imaging techniques, particularly gadolinium-enhanced MRI, is an integral part of the clinical management of brain tumors in veterinary patients. Spontaneous canine brain tumors are increasingly recognized and utilized as a translational model for the study of human brain tumors. However, no standardized neuroimaging response assessment criteria have been formulated for use in veterinary clinical trials. Previous studies have found that the pathophysiologic features inherent to brain tumors and the surrounding brain complicate the use of the response evaluation criteria in solid tumors (RECIST) assessment system. Objectives of this review are to describe strengths and limitations of published imaging-based brain tumor response criteria and propose a system for use in veterinary patients. The widely used human Macdonald and response assessment in neuro-oncology (RANO) criteria are reviewed and described as to how they can be applied to veterinary brain tumors. Discussion points will include current challenges associated with the interpretation of brain tumor therapeutic responses such as imaging pseudophenomena and treatment-induced necrosis, and how advancements in perfusion imaging, positron emission tomography, and magnetic resonance spectroscopy have shown promise in differentiating tumor progression from therapy-induced changes. Finally, although objective endpoints such as MR imaging and survival estimates will likely continue to comprise the foundations for outcome measures in veterinary brain tumor clinical trials, we propose that in order to provide a more relevant therapeutic response metric for veterinary patients, composite response systems should be formulated and validated that combine imaging and clinical assessment criteria. PMID:24219161

  6. INVITED REVIEW – NEUROIMAGING RESPONSE ASSESSMENT CRITERIA FOR BRAIN TUMORS IN VETERINARY PATIENTS

    PubMed Central

    Rossmeisl, John H.; Garcia, Paulo A.; Daniel, Gregory B.; Bourland, John Daniel; Debinski, Waldemar; Dervisis, Nikolaos; Klahn, Shawna

    2013-01-01

    The evaluation of therapeutic response using cross-sectional imaging techniques, particularly gadolinium-enhanced MRI, is an integral part of the clinical management of brain tumors in veterinary patients. Spontaneous canine brain tumors are increasingly recognized and utilized as a translational model for the study of human brain tumors. However, no standardized neuroimaging response assessment criteria have been formulated for use in veterinary clinical trials. Previous studies have found that the pathophysiologic features inherent to brain tumors and the surrounding brain complicate the use of the Response Evaluation Criteria in Solid Tumors (RECIST) assessment system. Objectives of this review are to describe strengths and limitations of published imaging-based brain tumor response criteria and propose a system for use in veterinary patients. The widely used human Macdonald and Response Assessment in Neuro-oncology (RANO) criteria are reviewed and described as to how they can be applied to veterinary brain tumors. Discussion points will include current challenges associated with the interpretation of brain tumor therapeutic responses such as imaging pseudophenomena and treatment-induced necrosis, and how advancements in perfusion imaging, positron emission tomography, and magnetic resonance spectroscopy have shown promise in differentiating tumor progression from therapy-induced changes. Finally, although objective endpoints such as MR-imaging and survival estimates will likely continue to comprise the foundations for outcome measures in veterinary brain tumor clinical trials, we propose that in order to provide a more relevant therapeutic response metric for veterinary patients, composite response systems should be formulated and validated that combine imaging and clinical assessment criteria. PMID:24219161

  7. A multicenter study of primary brain tumor incidence in Australia (2000–2008)

    PubMed Central

    Dobes, Martin; Shadbolt, Bruce; Khurana, Vini G.; Jain, Sanjiv; Smith, Sarah F.; Smee, Robert; Dexter, Mark; Cook, Raymond

    2011-01-01

    There are conflicting reports from Europe and North America regarding trends in the incidence of primary brain tumor, whereas the incidence of primary brain tumors in Australia is currently unknown. We aimed to determine the incidence in Australia with age-, sex-, and benign-versus-malignant histology-specific analyses. A multicenter study was performed in the state of New South Wales (NSW) and the Australian Capital Territory (ACT), which has a combined population of >7 million with >97% rate of population retention for medical care. We retrospectively mined pathology databases servicing neurosurgical centers in NSW and ACT for histologically confirmed primary brain tumors diagnosed from January 2000 through December 2008. Data were weighted for patient outflow and data completeness. Incidence rates were age standardized and trends analyzed using joinpoint analysis. A weighted total of 7651 primary brain tumors were analyzed. The overall US-standardized incidence of primary brain tumors was 11.3 cases 100 000 person-years (±0.13; 95% confidence interval, 9.8–12.3) during the study period with no significant linear increase. A significant increase in primary malignant brain tumors from 2000 to 2008 was observed; this appears to be largely due to an increase in malignant tumor incidence in the ≥65-year age group. This collection represents the most contemporary data on primary brain tumor incidence in Australia. Whether the observed increase in malignant primary brain tumors, particularly in persons aged ≥65 years, is due to improved detection, diagnosis, and care delivery or a true change in incidence remains undetermined. We recommend a direct, uniform, and centralized approach to monitoring primary brain tumor incidence that can be independent of multiple interstate cancer registries. PMID:21727214

  8. Type II fuzzy systems for amyloid plaque segmentation in transgenic mouse brains for Alzheimer's disease quantification

    NASA Astrophysics Data System (ADS)

    Khademi, April; Hosseinzadeh, Danoush

    2014-03-01

    Alzheimer's disease (AD) is the most common form of dementia in the elderly characterized by extracellular deposition of amyloid plaques (AP). Using animal models, AP loads have been manually measured from histological specimens to understand disease etiology, as well as response to treatment. Due to the manual nature of these approaches, obtaining the AP load is labourious, subjective and error prone. Automated algorithms can be designed to alleviate these challenges by objectively segmenting AP. In this paper, we focus on the development of a novel algorithm for AP segmentation based on robust preprocessing and a Type II fuzzy system. Type II fuzzy systems are much more advantageous over the traditional Type I fuzzy systems, since ambiguity in the membership function may be modeled and exploited to generate excellent segmentation results. The ambiguity in the membership function is defined as an adaptively changing parameter that is tuned based on the local contrast characteristics of the image. Using transgenic mouse brains with AP ground truth, validation studies were carried out showing a high degree of overlap and low degree of oversegmentation (0.8233 and 0.0917, respectively). The results highlight that such a framework is able to handle plaques of various types (diffuse, punctate), plaques with varying Aβ concentrations as well as intensity variation caused by treatment effects or staining variability.

  9. A fully automatic unsupervised segmentation framework for the brain tissues in MR images

    NASA Astrophysics Data System (ADS)

    Mahmood, Qaiser; Chodorowski, Artur; Ehteshami Bejnordi, Babak; Persson, Mikael

    2014-03-01

    This paper presents a novel fully automatic unsupervised framework for the segmentation of brain tissues in magnetic resonance (MR) images. The framework is a combination of our proposed Bayesian-based adaptive mean shift (BAMS), a priori spatial tissue probability maps and fuzzy c-means. BAMS is applied to cluster the tissues in the joint spatialintensity feature space and then a fuzzy c-means algorithm is employed with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue types; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The proposed framework is validated on multimodal synthetic as well as on real T1-weighted MR data with varying noise characteristics and spatial intensity inhomogeneity. The performance of the proposed framework is evaluated relative to our previous method BAMS and other existing adaptive mean shift framework. Both of these are based on the mode pruning and voxel weighted k-means algorithm for classifying the clusters into WM, GM and CSF tissue. The experimental results demonstrate the robustness of the proposed framework to noise and spatial intensity inhomogeneity, and that it exhibits a higher degree of segmentation accuracy in segmenting both synthetic and real MR data compared to competing methods.

  10. Vorinostat and Temozolomide in Treating Young Patients With Relapsed or Refractory Primary Brain Tumors or Spinal Cord Tumors

    ClinicalTrials.gov

    2013-05-01

    Childhood Atypical Teratoid/Rhabdoid Tumor; Childhood Central Nervous System Choriocarcinoma; Childhood Central Nervous System Embryonal Tumor; Childhood Central Nervous System Germinoma; Childhood Central Nervous System Mixed Germ Cell Tumor; Childhood Central Nervous System Teratoma; Childhood Central Nervous System Yolk Sac Tumor; Childhood Choroid Plexus Tumor; Childhood Craniopharyngioma; Childhood Ependymoblastoma; Childhood Grade I Meningioma; Childhood Grade II Meningioma; Childhood Grade III Meningioma; Childhood High-grade Cerebellar Astrocytoma; Childhood High-grade Cerebral Astrocytoma; Childhood Infratentorial Ependymoma; Childhood Low-grade Cerebellar Astrocytoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Medulloepithelioma; Childhood Mixed Glioma; Childhood Oligodendroglioma; Childhood Supratentorial Ependymoma; Extra-adrenal Paraganglioma; Recurrent Childhood Brain Stem Glioma; Recurrent Childhood Central Nervous System Embryonal Tumor; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Pineoblastoma; Recurrent Childhood Spinal Cord Neoplasm; Recurrent Childhood Subependymal Giant Cell Astrocytoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Childhood Visual Pathway and Hypothalamic Glioma

  11. Automated ensemble segmentation of epithelial proliferation, necrosis, and fibrosis using scatter tumor imaging

    NASA Astrophysics Data System (ADS)

    Garcia-Allende, P. Beatriz; Conde, Olga M.; Krishnaswamy, Venkataramanan; Hoopes, P. Jack; Pogue, Brian W.; Mirapeix, Jesus; Lopez-Higuera, Jose M.

    2010-04-01

    Conventional imaging systems used today in surgical settings rely on contrast enhancement based on color and intensity and they are not sensitive to morphology changes at the microscopic level. Elastic light scattering spectroscopy has been shown to distinguish ultra-structural changes in tissue. Therefore, it could provide this intrinsic contrast being enormously useful in guiding complex surgical interventions. Scatter parameters associated with epithelial proliferation, necrosis and fibrosis in pancreatic tumors were previously estimated in a quantitative manner. Subtle variations were encountered across the distinct diagnostic categories. This work proposes an automated methodology to correlate these variations with their corresponding tumor morphologies. A new approach based on the aggregation of the predictions of K-nearest neighbors (kNN) algorithm and Artificial Neural Networks (ANNs) has been developed. The major benefit obtained from the combination of the distinct classifiers is a significant increase in the number of pixel localizations whose corresponding tissue type is reliably assured. Pseudo-color diagnosis images are provided showing a strong correlation with sample segmentations performed by a veterinary pathologist.

  12. A small-molecule antagonist of CXCR4 inhibits intracranial growth of primary brain tumors

    NASA Astrophysics Data System (ADS)

    Rubin, Joshua B.; Kung, Andrew L.; Klein, Robyn S.; Chan, Jennifer A.; Sun, Yanping; Schmidt, Karl; Kieran, Mark W.; Luster, Andrew D.; Segal, Rosalind A.

    2003-11-01

    The vast majority of brain tumors in adults exhibit glial characteristics. Brain tumors in children are diverse: Many have neuronal characteristics, whereas others have glial features. Here we show that activation of the Gi protein-coupled receptor CXCR4 is critical for the growth of both malignant neuronal and glial tumors. Systemic administration of CXCR4 antagonist AMD 3100 inhibits growth of intracranial glioblastoma and medulloblastoma xenografts by increasing apoptosis and decreasing the proliferation of tumor cells. This reflects the ability of AMD 3100 to reduce the activation of extracellular signal-regulated kinases 1 and 2 and Akt, all of which are pathways downstream of CXCR4 that promote survival, proliferation, and migration. These studies (i) demonstrate that CXCR4 is critical to the progression of diverse brain malignances and (ii) provide a scientific rationale for clinical evaluation of AMD 3100 in treating both adults and children with malignant brain tumors.

  13. Prediction in the service of comprehension: modulated early brain responses to omitted speech segments.

    PubMed

    Bendixen, Alexandra; Scharinger, Mathias; Strauß, Antje; Obleser, Jonas

    2014-04-01

    Speech signals are often compromised by disruptions originating from external (e.g., masking noise) or internal (e.g., inaccurate articulation) sources. Speech comprehension thus entails detecting and replacing missing information based on predictive and restorative neural mechanisms. The present study targets predictive mechanisms by investigating the influence of a speech segment's predictability on early, modality-specific electrophysiological responses to this segment's omission. Predictability was manipulated in simple physical terms in a single-word framework (Experiment 1) or in more complex semantic terms in a sentence framework (Experiment 2). In both experiments, final consonants of the German words Lachs ([laks], salmon) or Latz ([lats], bib) were occasionally omitted, resulting in the syllable La ([la], no semantic meaning), while brain responses were measured with multi-channel electroencephalography (EEG). In both experiments, the occasional presentation of the fragment La elicited a larger omission response when the final speech segment had been predictable. The omission response occurred ∼125-165 msec after the expected onset of the final segment and showed characteristics of the omission mismatch negativity (MMN), with generators in auditory cortical areas. Suggestive of a general auditory predictive mechanism at work, this main observation was robust against varying source of predictive information or attentional allocation, differing between the two experiments. Source localization further suggested the omission response enhancement by predictability to emerge from left superior temporal gyrus and left angular gyrus in both experiments, with additional experiment-specific contributions. These results are consistent with the existence of predictive coding mechanisms in the central auditory system, and suggestive of the general predictive properties of the auditory system to support spoken word recognition. PMID:24561233

  14. Plasma Levels of Glucose and Insulin in Patients with Brain Tumors

    PubMed Central

    ALEXANDRU, OANA; ENE, L.; PURCARU, OANA STEFANA; TACHE, DANIELA ELISE; POPESCU, ALISA; NEAMTU, OANA MARIA; TATARANU, LIGIA GABRIELA; GEORGESCU, ADA MARIA; TUDORICA, VALERICA; ZAHARIA, CORNELIA; DRICU, ANICA

    2014-01-01

    In the last years there were many authors that suggest the existence of an association between different components of metabolic syndrome and various cancers. Two important components of metabolic syndrome are hyperglycemia and hyperinsulinemia. Both of them had already been linked with the increased risk of pancreatic, breast, endometrial or prostate cancer. However the correlation of the level of the glucose and insulin with various types and grades of brain tumors remains unclear. In this article we have analysed the values of plasma glucose and insulin in 267 patients, consecutively diagnosed with various types of brain tumors. Our results showed no correlation between the glycemia and brain tumor types or grades. High plasma levels of insulin were found in brain metastasis and astrocytomas while the other types of brain tumors (meningiomas and glioblastomas) had lower levels of the peptide. The levels of insulin were also higher in brain metastasis and grade 3 brain tumors when compared with grade 1, grade 2 and grade 4 brain tumors. PMID:24791202

  15. Metastasis Infiltration: An Investigation of the Postoperative Brain-Tumor Interface

    SciTech Connect

    Raore, Bethwel; Schniederjan, Matthew; Prabhu, Roshan; Brat, Daniel J.; Shu, Hui-Kuo; Olson, Jeffrey J.

    2011-11-15

    Purpose: This study aims to evaluate brain infiltration of metastatic tumor cells past the main tumor resection margin to assess the biological basis for the use of stereotactic radiosurgery treatment of the tumor resection cavity and visualized resection edge or clinical target volume. Methods and Materials: Resection margin tissue was obtained after gross total resection of a small group of metastatic lesions from a variety of primary sources. The tissue at the border of the tumor and brain tissue was carefully oriented and processed to evaluate the presence of tumor cells within brain tissue and their distance from the resection margin. Results: Microscopic assessment of the radially oriented tissue samples showed no tumor cells infiltrating the surrounding brain tissue. Among the positive findings were reactive astrocytosis observed on the brain tissue immediately adjacent to the tumor resection bed margin. Conclusions: The lack of evidence of metastatic tumor cell infiltration into surrounding brain suggests the need to target only a narrow depth of the resection cavity margin to minimize normal tissue injury and prevent treatment size-dependent stereotactic radiosurgery complications.

  16. Distribution of polymer nanoparticles by convection-enhanced delivery to brain tumors.

    PubMed

    Saucier-Sawyer, Jennifer K; Seo, Young-Eun; Gaudin, Alice; Quijano, Elias; Song, Eric; Sawyer, Andrew J; Deng, Yang; Huttner, Anita; Saltzman, W Mark

    2016-06-28

    Glioblastoma multiforme (GBM) is a fatal brain tumor characterized by infiltration beyond the margins of the main tumor mass and local recurrence after surgery. The blood-brain barrier (BBB) poses the most significant hurdle to brain tumor treatment. Convection-enhanced delivery (CED) allows for local administration of agents, overcoming the restrictions of the BBB. Recently, polymer nanoparticles have been demonstrated to penetrate readily through the healthy brain when delivered by CED, and size has been shown to be a critical factor for nanoparticle penetration. Because these brain-penetrating nanoparticles (BPNPs) have high potential for treatment of intracranial tumors since they offer the potential for cell targeting and controlled drug release after administration, here we investigated the intratumoral CED infusions of PLGA BPNPs in animals bearing either U87 or RG2 intracranial tumors. We demonstrate that the overall volume of distribution of these BPNPs was similar to that observed in healthy brains; however, the presence of tumors resulted in asymmetric and heterogeneous distribution patterns, with substantial leakage into the peritumoral tissue. Together, our results suggest that CED of BPNPs should be optimized by accounting for tumor geometry, in terms of location, size and presence of necrotic regions, to determine the ideal infusion site and parameters for individual tumors. PMID:27063424

  17. Awake brain tumor resection during pregnancy: Decision making and technical nuances.

    PubMed

    Meng, Lingzhong; Han, Seunggu J; Rollins, Mark D; Gelb, Adrian W; Chang, Edward F

    2016-02-01

    The co-occurrence of primary brain tumor and pregnancy poses unique challenges to the treating physician. If a rapidly growing lesion causes life-threatening mass effect, craniotomy for tumor debulking becomes urgent. The choice between awake craniotomy versus general anesthesia becomes complicated if the tumor is encroaching on eloquent brain because considerations pertinent to both patient safety and oncological outcome, in addition to fetal wellbeing, are involved. A 31-year-old female at 30 weeks gestation with twins presented to our hospital seeking awake craniotomy to resect a 7 × 6 × 5 cm left frontoparietal brain tumor with 7 mm left-to-right subfalcine herniation on imaging that led to word finding difficulty, dysfluency, right upper extremity paralysis, and right lower extremity weakness. She had twice undergone tumor debulking under general anesthesia during the same pregnancy at an outside hospital at 16 weeks and 28 weeks gestation. There were considerations both for and against awake brain tumor resection over surgery under general anesthesia. The decision-making process and the technical nuances related to awake brain tumor resection in this neurologically impaired patient are discussed. Awake craniotomy benefits the patient who harbors a tumor that encroaches on the eloquent brain by allowing a greater extent of resection while preserving the language and sensorimotor function. It can be successfully done in pregnant patients who are neurologically impaired. The patient should be motivated and well informed of the details of the process. A multidisciplinary and collaborative effort is also crucial. PMID:26498092

  18. Pediatric Brain Tumors: Innovative Genomic Information Is Transforming the Diagnostic and Clinical Landscape.

    PubMed

    Gajjar, Amar; Bowers, Daniel C; Karajannis, Matthias A; Leary, Sarah; Witt, Hendrik; Gottardo, Nicholas G

    2015-09-20

    Pediatric neuro-oncology has undergone an exciting and dramatic transformation during the past 5 years. This article summarizes data from collaborative group and institutional trials that have advanced the science of pediatric brain tumors and survival of patients with these tumors. Advanced genomic analysis of the entire spectrum of pediatric brain tumors has heralded an era in which stakeholders in the pediatric neuro-oncology community are being challenged to reconsider their current research and diagnostic and treatment strategies. The incorporation of this new information into the next-generation treatment protocols will unleash new challenges. This review succinctly summarizes the key advances in our understanding of the common pediatric brain tumors (ie, medulloblastoma, low- and high-grade gliomas, diffuse intrinsic pontine glioma, and ependymoma) and some selected rare tumors (ie, atypical teratoid/rhabdoid tumor and CNS primitive neuroectodermal tumor). The potential impact of this new information on future clinical protocols also is discussed. Cutting-edge genomics technologies and the information gained from such studies are facilitating the identification of molecularly defined subgroups within patients with particular pediatric brain tumors. The number of evaluable patients in each subgroup is small, particularly in the subgroups of rare diseases. Therefore, international collaboration will be crucial to draw meaningful conclusions about novel approaches to treating pediatric brain tumors. PMID:26304884

  19. SU-C-9A-03: Simultaneous Deconvolution and Segmentation for PET Tumor Delineation Using a Variational Method

    SciTech Connect

    Li, L; Tan, S; Lu, W; D'Souza, W

    2014-06-01

    Purpose: To implement a new method that integrates deconvolution with segmentation under the variational framework for PET tumor delineation. Methods: Deconvolution and segmentation are both challenging problems in image processing. The partial volume effect (PVE) makes tumor boundaries in PET image blurred which affects the accuracy of tumor segmentation. Deconvolution aims to obtain a PVE-free image, which can help to improve the segmentation accuracy. Conversely, a correct localization of the object boundaries is helpful to estimate the blur kernel, and thus assist in the deconvolution. In this study, we proposed to solve the two problems simultaneously using a variational method so that they can benefit each other. The energy functional consists of a fidelity term and a regularization term, and the blur kernel was limited to be the isotropic Gaussian kernel. We minimized the energy functional by solving the associated Euler-Lagrange equations and taking the derivative with respect to the parameters of the kernel function. An alternate minimization method was used to iterate between segmentation, deconvolution and blur-kernel recovery. The performance of the proposed method was tested on clinic PET images of patients with non-Hodgkin's lymphoma, and compared with seven other segmentation methods using the dice similarity index (DSI) and volume error (VE). Results: Among all segmentation methods, the proposed one (DSI=0.81, VE=0.05) has the highest accuracy, followed by the active contours without edges (DSI=0.81, VE=0.25), while other methods including the Graph Cut and the Mumford-Shah (MS) method have lower accuracy. A visual inspection shows that the proposed method localizes the real tumor contour very well. Conclusion: The result showed that deconvolution and segmentation can contribute to each other. The proposed variational method solve the two problems simultaneously, and leads to a high performance for tumor segmentation in PET. This work was supported

  20. Segmentation of MRI brain scans into gray matter, white matter, and CSF

    NASA Astrophysics Data System (ADS)

    Sandor, Tamas; Ong, Hoo-Tee; Valtchinov, Vladimir I.; Albert, Marilyn; Jolesz, Ferenc A.

    1997-04-01

    An algorithm is described that can separate gray matter, white matter and CSF in brain scans taken with 3DFFT T1- weighted gradient echo magnetic resonance imaging. Although the algorithm is fully automated, it requires brain contours as input that utilize user-defined features. The inter- and intra-operator errors stemming from the variability of the contour definition and affecting the segmentation were assessed by using coronal brain scans of 19 subjects. The inter-operator errors were (1.61 plus or minus 2.38)% (P equals 0.01) for gray matter, (0.31 plus or minus 2.06)% (P equals 0.53) for white matter and (0.28 plus or minus 3.84)% (P equals 0.76) for cerebrospinal fluid (CSF). the intra- operator error was (0.28 plus or minus 0.55)% (P greater than 0.04) for gray matter, (0.40 plus or minus 0.37)% (P equals 0.0002) for white matter and (0.26 plus or minus 1.31)% (P equals 0.39) for CSF.

  1. Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.

    1996-06-01

    This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.

  2. Brain tumor and psychiatric manifestations: a case report and brief review.

    PubMed

    Madhusoodanan, Subramoniam; Danan, Deepa; Brenner, Ronald; Bogunovic, Olivera

    2004-01-01

    Brain tumors may present multiple psychiatric symptoms such as depression, personality change, abulia, auditory and visual hallucinations, mania, panic attacks, or amnesia. A case of a 79-year-old woman who presented with depressive symptoms but showed minimal neurological signs and symptoms is discussed. Neuroimaging revealed a brain tumor in the left parietal lobe, and patient underwent neurosurgical treatment and subsequently received chemotherapy and radiation. Some patients with neurologically silent brain tumors may present with psychiatric symptoms only. Therefore, we emphasize the consideration of neuroimaging in patients with a change in mental status regardless of a lack of neurological symptoms. PMID:15328904

  3. Neuronavigation in the surgical management of brain tumors: current and future trends

    PubMed Central

    Orringer, Daniel A; Golby, Alexandra; Jolesz, Ferenc

    2013-01-01

    Neuronavigation has become an ubiquitous tool in the surgical management of brain tumors. This review describes the use and limitations of current neuronavigational systems for brain tumor biopsy and resection. Methods for integrating intraoperative imaging into neuronavigational datasets developed to address the diminishing accuracy of positional information that occurs over the course of brain tumor resection are discussed. In addition, the process of integration of functional MRI and tractography into navigational models is reviewed. Finally, emerging concepts and future challenges relating to the development and implementation of experimental imaging technologies in the navigational environment are explored. PMID:23116076

  4. The diagnostic accuracy of multiparametric MRI to determine pediatric brain tumor grades and types.

    PubMed

    Koob, Mériam; Girard, Nadine; Ghattas, Badih; Fellah, Slim; Confort-Gouny, Sylviane; Figarella-Branger, Dominique; Scavarda, Didier

    2016-04-01

    Childhood brain tumors show great histological variability. The goal of this retrospective study was to assess the diagnostic accuracy of multimodal MR imaging (diffusion, perfusion, MR spectroscopy) in the distinction of pediatric brain tumor grades and types. Seventy-six patients (range 1 month to 18 years) with brain tumors underwent multimodal MR imaging. Tumors were categorized by grade (I-IV) and by histological type (A-H). Multivariate statistical analysis was performed to evaluate the diagnostic accuracy of single and combined MR modalities, and of single imaging parameters to distinguish the different groups. The highest diagnostic accuracy for tumor grading was obtained with diffusion-perfusion (73.24 %) and for tumor typing with diffusion-perfusion-MR spectroscopy (55.76 %). The best diagnostic accuracy was obtained for tumor grading in I and IV and for tumor typing in embryonal tumor and pilocytic astrocytoma. Poor accuracy was seen in other grades and types. ADC and rADC were the best parameters for tumor grading and typing followed by choline level with an intermediate echo time, CBV for grading and Tmax for typing. Multiparametric MR imaging can be accurate in determining tumor grades (primarily grades I and IV) and types (mainly pilocytic astrocytomas and embryonal tumors) in children. PMID:26732081

  5. Ex vivo brain tumor analysis using spectroscopic optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Lenz, Marcel; Krug, Robin; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

    2016-03-01

    A big challenge during neurosurgeries is to distinguish between healthy tissue and cancerous tissue, but currently a suitable non-invasive real time imaging modality is not available. Optical Coherence Tomography (OCT) is a potential technique for such a modality. OCT has a penetration depth of 1-2 mm and a resolution of 1-15 μm which is sufficient to illustrate structural differences between healthy tissue and brain tumor. Therefore, we investigated gray and white matter of healthy central nervous system and meningioma samples with a Spectral Domain OCT System (Thorlabs Callisto). Additional OCT images were generated after paraffin embedding and after the samples were cut into 10 μm thin slices for histological investigation with a bright field microscope. All samples were stained with Hematoxylin and Eosin. In all cases B-scans and 3D images were made. Furthermore, a camera image of the investigated area was made by the built-in video camera of our OCT system. For orientation, the backsides of all samples were marked with blue ink. The structural differences between healthy tissue and meningioma samples were most pronounced directly after removal. After paraffin embedding these differences diminished. A correlation between OCT en face images and microscopy images can be seen. In order to increase contrast, post processing algorithms were applied. Hence we employed Spectroscopic OCT, pattern recognition algorithms and machine learning algorithms such as k-means Clustering and Principal Component Analysis.

  6. Cellular microenvironment modulates the galvanotaxis of brain tumor initiating cells

    PubMed Central

    Huang, Yu-Ja; Hoffmann, Gwendolyn; Wheeler, Benjamin; Schiapparelli, Paula; Quinones-Hinojosa, Alfredo; Searson, Peter

    2016-01-01

    Galvanotaxis is a complex process that represents the collective outcome of various contributing mechanisms, including asymmetric ion influxes, preferential activation of voltage-gated channels, and electrophoretic redistribution of membrane components. While a large number of studies have focused on various up- and downstream signaling pathways, little is known about how the surrounding microenvironment may interact and contribute to the directional response. Using a customized galvanotaxis chip capable of carrying out experiments in both two- and three-dimensional microenvironments, we show that cell-extracellular matrix (ECM) interactions modulate the galvanotaxis of brain tumor initiating cells (BTICs). Five different BTICs across three different glioblastoma subtypes were examined and shown to all migrate toward the anode in the presence of a direct-current electric field (dcEF) when cultured on a poly-L-ornithine/laminin coated surface, while the fetal-derived neural progenitor cells (fNPCs) migrated toward the cathode. Interestingly, when embedded in a 3D ECM composed of hyaluronic acid and collagen, BTICs exhibited opposite directional response and migrated toward the cathode. Pharmacological inhibition against a panel of key molecules involved in galvanotaxis further revealed the mechanistic differences between 2- and 3D galvanotaxis in BTICs. Both myosin II and phosphoinositide 3-kinase (PI3K) were found to hold strikingly different roles in different microenvironments. PMID:26898606

  7. Nanoparticle-Mediated Photothermal Therapy of Brain Tumors

    NASA Astrophysics Data System (ADS)

    Makkouk, Amani R.; Madsen, Steen J.

    Nanoparticles (10-1,000 nm diameter) have been investigated for use in numerous diagnostic and therapeutic applications. Gold nanoparticles are particularly appealing due to their biological inertness and the ability to conjugate a wide variety of ligands to their surface. Additionally, their optical properties can be tuned through variations of their size, shape, and composition. For example, gold-silica nanoshells, consisting of a spherical dielectric silica core (100-120 nm diameter) surrounded by a 10-20 nm gold shell, have a strong resonant absorption at approximately 800 nm where light has significant penetration in biological tissues. Following light absorption, surface electrons are photoexcited and the resultant heated electron gas is dissipated to the surrounding medium causing thermal damage. The ability of nanoparticles to convert optical energy to thermal energy makes them ideally suited for photothermal therapy (PTT). This review focuses on the utility of gold-silica nanoshells in PTT of brain tumors. PTT has proven effective in a number of in vitro and in vivo studies. Of particular clinical relevance are results demonstrating PTT efficacy in an orthotopic canine model.

  8. Thyroid function after treatment of brain tumors in children.

    PubMed

    Ogilvy-Stuart, A L; Shalet, S M; Gattamaneni, H R

    1991-11-01

    In 134 children who had been treated for a brain tumor not involving the hypothalamic-pituitary axis, thyroid function was assessed up to 24 years afte