Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)最新文献_第3页

Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries. 创伤性脑损伤的成对正序离群点检测。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2018-01-01 Epub Date: 2018-02-17 DOI: 10.1007/978-3-319-75238-9_9

Matt Higger, Martha Shenton, Sylvain Bouix

引用次数: 0

Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation. 关节强度融合图像合成在多发性硬化病灶分割中的应用。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2018-01-01 Epub Date: 2018-02-17

Greg M Fleishman, Alessandra Valcarcel, Dzung L Pham, Snehashis Roy, Peter A Calabresi, Paul Yushkevich, Russell T Shinohara, Ipek Oguz

{"title":"Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.","authors":"Greg M Fleishman, Alessandra Valcarcel, Dzung L Pham, Snehashis Roy, Peter A Calabresi, Paul Yushkevich, Russell T Shinohara, Ipek Oguz","doi":"","DOIUrl":"","url":null,"abstract":"We propose a new approach to Multiple Sclerosis lesion segmentation that utilizes synthesized images. A new method of image synthesis is considered: joint intensity fusion (JIF). JIF synthesizes an image from a library of deformably registered and intensity normalized atlases. Each location in the synthesized image is a weighted average of the registered atlases; atlas weights vary spatially. The weights are determined using the joint label fusion (JLF) framework. The primary methodological contribution is the application of JLF to MRI signal directly rather than labels. Synthesized images are then used as additional features in a lesion segmentation task using the OASIS classifier, a logistic regression model on intensities from multiple modalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by 0.0462 ± 0.0050 (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":"10670 ","pages":"43-54"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5920684/pdf/nihms960389.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36058091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma. 脑癌成像表型组学工具包(Brain - captk):胶质母细胞瘤定量分析的互动平台。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2018-01-01 Epub Date: 2018-02-17 DOI: 10.1007/978-3-319-75238-9_12

Saima Rathore, Spyridon Bakas, Sarthak Pati, Hamed Akbari, Ratheesh Kalarot, Patmaa Sridharan, Martin Rozycki, Mark Bergman, Birkan Tunc, Ragini Verma, Michel Bilello, Christos Davatzikos

{"title":"Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.","authors":"Saima Rathore, Spyridon Bakas, Sarthak Pati, Hamed Akbari, Ratheesh Kalarot, Patmaa Sridharan, Martin Rozycki, Mark Bergman, Birkan Tunc, Ragini Verma, Michel Bilello, Christos Davatzikos","doi":"10.1007/978-3-319-75238-9_12","DOIUrl":"https://doi.org/10.1007/978-3-319-75238-9_12","url":null,"abstract":"Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":"10670 ","pages":"133-145"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-75238-9_12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36078131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 50

Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation 关节强度融合图像合成在多发性硬化病灶分割中的应用

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2017-09-14 DOI: 10.1007/978-3-319-75238-9_4

G. Fleishman, A. Valcarcel, D. Pham, Snehashis Roy, P. Calabresi, Paul Yushkevich, R. Shinohara, I. Oguz

引用次数: 7

Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury. 多模式登记提高儿童创伤性脑损伤的群体歧视。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2016-10-01 DOI: 10.1007/978-3-319-55524-9_4

Emily L Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C Giza, Robert F Asarnow, Paul M Thompson

{"title":"Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury.","authors":"Emily L Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C Giza, Robert F Asarnow, Paul M Thompson","doi":"10.1007/978-3-319-55524-9_4","DOIUrl":"https://doi.org/10.1007/978-3-319-55524-9_4","url":null,"abstract":"Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T1-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts.","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":"10154 ","pages":"32-42"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-55524-9_4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35618602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework. 基于混合生成-判别框架的术前和术后多模态磁共振成像卷中胶质瘤的分割。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2016-10-01 Epub Date: 2017-04-12 DOI: 10.1007/978-3-319-55524-9_18

Ke Zeng, Spyridon Bakas, Aristeidis Sotiras, Hamed Akbari, Martin Rozycki, Saima Rathore, Sarthak Pati, Christos Davatzikos

{"title":"Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework.","authors":"Ke Zeng, Spyridon Bakas, Aristeidis Sotiras, Hamed Akbari, Martin Rozycki, Saima Rathore, Sarthak Pati, Christos Davatzikos","doi":"10.1007/978-3-319-55524-9_18","DOIUrl":"10.1007/978-3-319-55524-9_18","url":null,"abstract":"We present an approach for segmenting both low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed framework is an extension of our previous work [6,7], with an additional component for segmenting post-operative scans. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative model based on a joint segmentation-registration framework is used to segment the brain scans into cancerous and healthy tissues. Secondly, a gradient boosting classification scheme is used to refine tumor segmentation based on information from multiple patients. We evaluated our approach in 218 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2016 challenge and report promising results. During the testing phase, the proposed approach was ranked among the top performing methods, after being additionally evaluated in 191 unseen cases.","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":"10154 ","pages":"184-194"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512606/pdf/nihms867688.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35184503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fast Approach to Automatic Detection of Brain Lesions. 一种快速自动检测脑损伤的方法。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2016-01-01 Epub Date: 2017-04-12 DOI: 10.1007/978-3-319-55524-9_6

Subhranil Koley, Chandan Chakraborty, Caterina Mainero, Bruce Fischl, Iman Aganj

{"title":"A Fast Approach to Automatic Detection of Brain Lesions.","authors":"Subhranil Koley, Chandan Chakraborty, Caterina Mainero, Bruce Fischl, Iman Aganj","doi":"10.1007/978-3-319-55524-9_6","DOIUrl":"https://doi.org/10.1007/978-3-319-55524-9_6","url":null,"abstract":"Template matching is a popular approach to computer-aided detection of brain lesions from magnetic resonance (MR) images. The outcomes are often sufficient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in situations in which time is crucial (e.g. emergent stroke). In view of this, we make use of 3D Gaussian templates with varying radii and propose a new method to compute the normalized cross-correlation coefficient as a similarity metric between the MR volume and the template to detect brain lesions. Contrary to the conventional fast Fourier transform (FFT) based approach, whose runtime grows as O(N logN) with the number of voxels, the proposed method computes the cross-correlation in O(N). We show through our experiments that the proposed method outperforms the FFT approach in terms of computational time, and retains comparable accuracy.","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":"10154 ","pages":"52-61"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-55524-9_6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35649497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation. GLISTRboost:将多模态MRI分割、配准和生物物理肿瘤生长建模与梯度增强机相结合，用于胶质瘤分割。

Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) Pub Date : 2016-01-01 DOI: 10.1007/978-3-319-30858-6_1

Spyridon Bakas, Ke Zeng, Aristeidis Sotiras, Saima Rathore, Hamed Akbari, Bilwaj Gaonkar, Martin Rozycki, Sarthak Pati, Christos Davatzikos

{"title":"GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.","authors":"Spyridon Bakas, Ke Zeng, Aristeidis Sotiras, Saima Rathore, Hamed Akbari, Bilwaj Gaonkar, Martin Rozycki, Sarthak Pati, Christos Davatzikos","doi":"10.1007/978-3-319-30858-6_1","DOIUrl":"https://doi.org/10.1007/978-3-319-30858-6_1","url":null,"abstract":"We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":"9556 ","pages":"144-155"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-30858-6_1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35184502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20