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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

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Alessandro Crimi

    1. African Institute of Mathematical Sciences, Cape Town, Ghana

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  2. Spyridon Bakas

    1. University of Pennsylvania, Philadelphia, USA

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  3. Hugo Kuijf

    1. University Medical Center Utrecht, Utrecht, The Netherlands

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  4. Bjoern Menze

    1. Technical University Munich, Munich, Germany

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  5. Mauricio Reyes

    1. Universität Bern, Bern, Switzerland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10670)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: BrainLes 2017.

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Table of contents (42 papers)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  2. Invited Talks

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation

      • Ipek Oguz, Aaron Carass, Dzung L. Pham, Snehashis Roy, Nagesh Subbana, Peter A. Calabresi et al.
      Pages 3-14

    3. Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials

      • Andrew Doyle, Colm Elliott, Zahra Karimaghaloo, Nagesh Subbanna, Douglas L. Arnold, Tal Arbel
      Pages 15-28

  3. Brain Lesion Image Analysis

    1. Front Matter

      Pages 29-29
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    2. Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Units

      • Simon Andermatt, Simon Pezold, Philippe C. Cattin
      Pages 31-42

    3. Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation

      • Greg M. Fleishman, Alessandra Valcarcel, Dzung L. Pham, Snehashis Roy, Peter A. Calabresi, Paul Yushkevich et al.
      Pages 43-54

    4. MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection

      • Nima Masoumi, Yiming Xiao, Hassan Rivaz
      Pages 55-63

    5. Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks

      • Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera, Jinendra Ekanayake, Neil Kitchen, Sébastien Ourselin et al.
      Pages 64-76

    6. Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework

      • Guoqing Wu, Yuanyuan Wang, Jinhua Yu
      Pages 77-87

    7. Traumatic Brain Lesion Quantification Based on Mean Diffusivity Changes

      • Christophe Maggia, Thomas Mistral, Senan Doyle, Florence Forbes, Alexandre Krainik, Damien Galanaud et al.
      Pages 88-99

    8. Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries

      • Matt Higger, Martha Shenton, Sylvain Bouix
      Pages 100-110

    9. Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation

      • Senan Doyle, Florence Forbes, Assia Jaillard, Olivier Heck, Olivier Detante, Michel Dojat
      Pages 111-122

    10. Brain Tumor Segmentation Using an Adversarial Network

      • Zeju Li, Yuanyuan Wang, Jinhua Yu
      Pages 123-132

    11. Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma

      • Saima Rathore, Spyridon Bakas, Sarthak Pati, Hamed Akbari, Ratheesh Kalarot, Patmaa Sridharan et al.
      Pages 133-145

  4. Brain Tumor Image Segmentation

    1. Front Matter

      Pages 147-147
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    2. Deep Learning Based Multimodal Brain Tumor Diagnosis

      • Yuexiang Li, Linlin Shen
      Pages 149-158

    3. Multimodal Brain Tumor Segmentation Using Ensemble of Forest Method

      • Ashish Phophalia, Pradipta Maji
      Pages 159-168

    4. Pooling-Free Fully Convolutional Networks with Dense Skip Connections for Semantic Segmentation, with Application to Brain Tumor Segmentation

      • Richard McKinley, Alain Jungo, Roland Wiest, Mauricio Reyes
      Pages 169-177

    5. Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks

      • Guotai Wang, Wenqi Li, Sébastien Ourselin, Tom Vercauteren
      Pages 178-190
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Other volumes

  1. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Keywords

About this book

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.

Editors and Affiliations

  • African Institute of Mathematical Sciences, Cape Town, Ghana

    Alessandro Crimi

  • University of Pennsylvania, Philadelphia, USA

    Spyridon Bakas

  • University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  • Technical University Munich, Munich, Germany

    Bjoern Menze

  • Universität Bern, Bern, Switzerland

    Mauricio Reyes

Bibliographic Information

  • Book Title: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

  • Book Subtitle: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers

  • Editors: Alessandro Crimi, Spyridon Bakas, Hugo Kuijf, Bjoern Menze, Mauricio Reyes

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-75238-9

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2018

  • Softcover ISBN: 978-3-319-75237-2Published: 17 February 2018

  • eBook ISBN: 978-3-319-75238-9Published: 16 February 2018

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIII, 517

  • Number of Illustrations: 233 b/w illustrations

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Probability and Statistics in Computer Science, Health Informatics

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