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

Table of Contents

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   Book Overview
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   Chapter 1 Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation
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   Chapter 2 Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials
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   Chapter 3 Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Units
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   Chapter 4 Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation
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   Chapter 5 MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection
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   Chapter 6 Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
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   Chapter 7 Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework
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   Chapter 8 Traumatic Brain Lesion Quantification Based on Mean Diffusivity Changes
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    Chapter 9 Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries
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    Chapter 10 Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation
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    Chapter 11 Brain Tumor Segmentation Using an Adversarial Network
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    Chapter 12 Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma
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    Chapter 13 Deep Learning Based Multimodal Brain Tumor Diagnosis
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    Chapter 14 Multimodal Brain Tumor Segmentation Using Ensemble of Forest Method
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    Chapter 15 Pooling-Free Fully Convolutional Networks with Dense Skip Connections for Semantic Segmentation, with Application to Brain Tumor Segmentation
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    Chapter 16 Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks
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    Chapter 17 3D Brain Tumor Segmentation Through Integrating Multiple 2D FCNNs
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    Chapter 18 MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks
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    Chapter 19 Automatic Segmentation and Overall Survival Prediction in Gliomas Using Fully Convolutional Neural Network and Texture Analysis
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    Chapter 20 Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks
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    Chapter 21 A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
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    Chapter 22 Dilated Convolutions for Brain Tumor Segmentation in MRI Scans
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    Chapter 23 Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation
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    Chapter 24 TPCNN: Two-Phase Patch-Based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction
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    Chapter 25 Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
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    Chapter 26 Multi-modal PixelNet for Brain Tumor Segmentation
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    Chapter 27 Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
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    Chapter 28 Brain Tumor Segmentation in MRI Scans Using Deeply-Supervised Neural Networks
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    Chapter 29 Brain Tumor Segmentation and Parsing on MRIs Using Multiresolution Neural Networks
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    Chapter 30 Brain Tumor Segmentation Using Deep Fully Convolutional Neural Networks
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    Chapter 31 Glioblastoma and Survival Prediction
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    Chapter 32 MRI Augmentation via Elastic Registration for Brain Lesions Segmentation
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    Chapter 33 Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation
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    Chapter 34 Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss
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    Chapter 35 Brain Tumor Segmentation Using a Multi-path CNN Based Method
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    Chapter 36 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences
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    Chapter 37 Automated Brain Tumor Segmentation on Magnetic Resonance Images and Patient’s Overall Survival Prediction Using Support Vector Machines
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    Chapter 38 Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
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    Chapter 39 Tumor Segmentation from Multimodal MRI Using Random Forest with Superpixel and Tensor Based Feature Extraction
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    Chapter 40 Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction
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    Chapter 41 WMH Segmentation Challenge: A Texture-Based Classification Approach
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    Chapter 42 White Matter Hyperintensities Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning