Brainlesion: Glioma, 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 / edited by Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, Mauricio Reyes.

Format:

Book

Contributor:

Crimi, Alessandro, Editor.

Bakas, Spyridon, Editor.

Kuijf, Hugo, Editor.

Menze, Bjoern, Editor.

Reyes, Mauricio, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 10670

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 10670

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer science-Mathematics.

Mathematical statistics.

Medical informatics.

Computer Vision.

Artificial Intelligence.

Probability and Statistics in Computer Science.

Health Informatics.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Probability and Statistics in Computer Science.

Health Informatics.

Physical Description:

1 online resource (XIII, 517 pages) : 233 illustrations

Edition:

1st ed. 2018.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

This 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.

Contents:

Invited 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 and 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.

Other Format:

Printed edition:

ISBN:

978-3-319-75238-9

9783319752389

Access Restriction:

Restricted for use by site license.