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

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

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. 

• brain image analysis
• complex networks
• image processing
• medical imaging
• probabilistic model
• brain cancer
• clinicalprognosis
• database
• deep learning
• deep neural networks
• glioma
• graph theory
• image segmentation
• life and medical sciences
• MRI
• multiple sclerosis
• segmentation
• statistical pattern recognition
• trauma brain injuries
• validation

• 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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