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

Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers

  • Conference proceedings
  • © 2016

Overview

Editors:
  1. Alessandro Crimi

    1. University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

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  2. Bjoern Menze

    1. TU München, Computer Science, Munich, Germany

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  3. Oskar Maier

    1. Medical Informatics, University of Lübeck, Lübeck, Germany

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  4. Mauricio Reyes

    1. Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

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  5. Stefan Winzeck

    1. Department of Medicine, University of Cambridge, Cambridge, United Kingdom

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  6. Heinz Handels

    1. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10154)

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 2016.

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About this book

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on  Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016.

The 26 papers presented in this volume were carefully reviewed.  They present the latest advances in segmentation, disease prognosis and other applications to the clinical context. 

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Keywords

Table of contents (26 papers)

  1. Front Matter

    Pages I-XI
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  2. Brain Lesion Image Analysis

    1. Front Matter

      Pages 1-1
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    2. Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting

      • Ferran Prados, M. Jorge Cardoso, Niamh Cawley, Baris Kanber, Olga Ciccarelli, Claudia A. M. Gandini Wheeler-Kingshott et al.
      Pages 3-15

    3. Towards a Second Brain Images of Tumours for Evaluation (BITE2) Database

      • I. J. Gerard, C. Couturier, M. Kersten-Oertel, S. Drouin, D. De Nigris, J. A. Hall et al.
      Pages 16-22

    4. Topological Measures of Connectomics for Low Grades Glioma

      • Benjamin Amoah, Alessandro Crimi
      Pages 23-31

    5. Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury

      • Emily L. Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian et al.
      Pages 32-42

    6. An Online Platform for the Automatic Reporting of Multi-parametric Tissue Signatures: A Case Study in Glioblastoma

      • Javier Juan-Albarracín, Elies Fuster-Garcia, Juan M. García-Gómez
      Pages 43-51

    7. A Fast Approach to Automatic Detection of Brain Lesions

      • Subhranil Koley, Chandan Chakraborty, Caterina Mainero, Bruce Fischl, Iman Aganj
      Pages 52-61

  3. Brain Tumor Image Segmentation

    1. Front Matter

      Pages 63-63
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    2. Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

      • Ramandeep S. Randhawa, Ankit Modi, Parag Jain, Prashant Warier
      Pages 65-74

    3. Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields

      • Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yong Fan, Yazhuo Zhang
      Pages 75-87

    4. Brain Tumor Segmentation with Optimized Random Forest

      • László Lefkovits, Szidónia Lefkovits, László Szilágyi
      Pages 88-99

    5. CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

      • Raphael Meier, Urspeter Knecht, Roland Wiest, Mauricio Reyes
      Pages 100-107

    6. Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation

      • Peter D. Chang
      Pages 108-118

    7. Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation

      • Richard McKinley, Rik Wepfer, Tom Gundersen, Franca Wagner, Andrew Chan, Roland Wiest et al.
      Pages 119-128

    8. Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patients

      • Abdelrahman Ellwaa, Ahmed Hussein, Essam AlNaggar, Mahmoud Zidan, Michael Zaki, Mohamed A. Ismail et al.
      Pages 129-137

    9. DeepMedic for Brain Tumor Segmentation

      • Konstantinos Kamnitsas, Enzo Ferrante, Sarah Parisot, Christian Ledig, Aditya V. Nori, Antonio Criminisi et al.
      Pages 138-149

    10. 3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

      • Adrià Casamitjana, Santi Puch, Asier Aduriz, Verónica Vilaplana
      Pages 150-161

    11. Anatomy-Guided Brain Tumor Segmentation and Classification

      • Bi Song, Chen-Rui Chou, Xiaojing Chen, Albert Huang, Ming-Chang Liu
      Pages 162-170

    12. Lifted Auto-Context Forests for Brain Tumour Segmentation

      • Loic Le Folgoc, Aditya V. Nori, Siddharth Ancha, Antonio Criminisi
      Pages 171-183
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Other volumes

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

Editors and Affiliations

  • University of Zurich, Istituto Italiano di Tecnologia, Genoa, Italy

    Alessandro Crimi

  • TU München, Computer Science, Munich, Germany

    Bjoern Menze

  • Medical Informatics, University of Lübeck, Lübeck, Germany

    Oskar Maier

  • Surgical Technology and Biomechanics, Universität Bern, Bern, Switzerland

    Mauricio Reyes

  • Department of Medicine, University of Cambridge, Cambridge, United Kingdom

    Stefan Winzeck

  • Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

    Heinz Handels

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