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

Chapter 1 Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting

Chapter 2 Towards a Second Brain Images of Tumours for Evaluation (BITE2) Database

Chapter 3 Topological Measures of Connectomics for Low Grades Glioma

Chapter 4 Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury

Chapter 5 An Online Platform for the Automatic Reporting of Multi-parametric Tissue Signatures: A Case Study in Glioblastoma

Chapter 6 A Fast Approach to Automatic Detection of Brain Lesions

Chapter 7 Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

Chapter 8 Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields

Chapter 9 Brain Tumor Segmentation with Optimized Random Forest

Chapter 10 CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

Chapter 11 Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation

Chapter 12 Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation

Chapter 13 Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected Patients

Chapter 15 3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

Chapter 16 Anatomy-Guided Brain Tumor Segmentation and Classification

Chapter 17 Lifted Auto-Context Forests for Brain Tumour Segmentation

Chapter 18 Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework

Chapter 19 Interactive Semi-automated Method Using Non-negative Matrix Factorization and Level Set Segmentation for the BRATS Challenge

Chapter 20 Brain Tumor Segmentation by Variability Characterization of Tumor Boundaries

Chapter 21 Predicting Stroke Lesion and Clinical Outcome with Random Forests

Chapter 22 Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke

Chapter 23 Prediction of Ischemic Stroke Lesion and Clinical Outcome in Multi-modal MRI Images Using Random Forests

Chapter 24 Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury Outcome

Chapter 25 Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn Methods

Chapter 26 Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury