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000826363 019__ $$a1029065043$$a1030291149
000826363 020__ $$a9783319752389$$q(electronic book)
000826363 020__ $$a3319752383$$q(electronic book)
000826363 020__ $$z9783319752372
000826363 0247_ $$a10.1007/978-3-319-75238-9$$2doi
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000826363 1112_ $$aBrainLes (Workshop)$$n(3rd :$$d2017 :$$cQuébec, Québec)
000826363 24510 $$aBrainlesion :$$bglioma, multiple sclerosis, stroke and traumatic brain injuries : third International Workshop, BrainLes 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised selected papers /$$cAlessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, Mauricio Reyes (eds.).
000826363 2463_ $$aBrainLes 2017
000826363 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000826363 300__ $$a1 online resource (xiii, 517 pages) :$$billustrations.
000826363 336__ $$atext$$btxt$$2rdacontent
000826363 337__ $$acomputer$$bc$$2rdamedia
000826363 338__ $$aonline resource$$bcr$$2rdacarrier
000826363 347__ $$atext file$$bPDF$$2rda
000826363 4901_ $$aLecture notes in computer science,$$x0302-9743 ;$$v10670
000826363 4901_ $$aLNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
000826363 500__ $$aIncludes author index.
000826363 5050_ $$aInvited Talks -- Dice overlap measures for objects of unknown number: Application to lesion segmentation -- Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials -- Brain Lesion Image Analysis -- Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units -- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation -- MARCEL (inter-Modality Ane Registration with CorELation ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection -- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks -- Overall Survival Time Prediction for High Grade Gliomas based on Sparse Representation Framework -- Traumatic Brain Lesion Quantication based on Mean Diusivity Changes -- Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries -- Sub-Acute & Chronic Ischemic Stroke Lesion MRI Segmentation -- Brain Tumor Segmentation Using an Adversarial Network -- Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma -- Brain Tumor Image Segmentation -- Deep Learning based Multimodal Brain Tumor Diagnosis -- Multimodal Brain Tumor Segmentation using Ensemble of Forest Method -- Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation -- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks -- 3D Brain Tumor Segmentation through Integrating Multiple 2D FCNNs -- MRI Brain Tumor Segmentation and Patient Survival Prediction using Random Forests and Fully Convolutional Networks -- Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis -- Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks -- A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor -- Dilated Convolutions for Brain Tumor Segmentation in MRI Scans -- Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation -- TPCNN: Two-phase Patch-based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction -- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge -- Multi-modal PixelNet for Brain Tumor Segmentation -- Brain Tumor Segmentation using Dense Fully Convolutional Neural Network -- Brain Tumor Segmentation in MRI Scans using Deeply-Supervised Neural Networks -- Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks -- Brain Tumor Segmentation using Deep Fully Convolutional Neural Networks -- Glioblastoma and Survival Prediction -- MRI Augmentation via Elastic Registration for Brain Lesions Segmentation -- Cascaded V-Net using ROI masks for brain tumor segmentation -- Brain Tumor Segmentation using a 3D FCN with Multi-Scale Loss -- Brain tumor segmentation using a multi-path CNN based method -- 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences -- Automated Brain Tumor Segmentation on Magnetic Resonance Images (MRIs) and Patient Overall Survival Prediction using Support Vector Machines -- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation -- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction -- Towards Uncertainty-assisted Brain Tumor Segmentation and Survival Prediction -- Ischemic Stroke Lesion Image Segmentation -- WMH Segmentation Challenge: a Texture-based Classication Approach -- White Matter Hyperintensities Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning.
000826363 506__ $$aAccess limited to authorized users.
000826363 520__ $$aThis book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.
000826363 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 19, 2018).
000826363 650_0 $$aBrain$$xTumors$$vCongresses.
000826363 650_0 $$aBrain$$xWounds and injuries$$vCongresses.
000826363 650_0 $$aCerebrovascular disease$$vCongresses.
000826363 7001_ $$aCrimi, Alessandro,$$eeditor.
000826363 7001_ $$aBakas, Spyridon,$$eeditor.
000826363 7001_ $$aKuijf, Hugo,$$eeditor.
000826363 7001_ $$aMenze, Bjoern,$$eeditor.
000826363 7001_ $$aReyes, Mauricio,$$eeditor.
000826363 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(20th :$$d2017 :$$cQuébec, Québec),$$jjointly held conference.
000826363 77608 $$iPrint version: $$z9783319752372
000826363 830_0 $$aLecture notes in computer science ;$$v10670.
000826363 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000826363 852__ $$bebk
000826363 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-75238-9$$zOnline Access$$91397441.1
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000826363 980__ $$aBIB
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