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

Table of Contents

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   Book Overview
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   Chapter 1 Multimodal Patho-Connectomics of Brain Injury
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   Chapter 2 CT Brain Perfusion: A Clinical Perspective
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   Chapter 3 Adverse Effects of Image Tiling on Convolutional Neural Networks
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   Chapter 4 An Update on Machine Learning in Neuro-Oncology Diagnostics
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   Chapter 5 MIMoSA: An Approach to Automatically Segment T2 Hyperintense and T1 Hypointense Lesions in Multiple Sclerosis
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   Chapter 6 CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels
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   Chapter 7 Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks
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   Chapter 8 Multipath Densely Connected Convolutional Neural Network for Brain Tumor Segmentation
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    Chapter 9 Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
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    Chapter 10 Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI Scans
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    Chapter 11 Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-Specific Interactive Refinement
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    Chapter 12 Characterizing Peritumoral Tissue Using DTI-Based Free Water Elimination
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    Chapter 13 Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI
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    Chapter 14 Shallow vs Deep Learning Architectures for White Matter Lesion Segmentation in the Early Stages of Multiple Sclerosis
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    Chapter 15 Detection of Midline Brain Abnormalities Using Convolutional Neural Networks
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    Chapter 16 Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
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    Chapter 17 Brain Tumor Detection and Classification from Multi-sequence MRI: Study Using ConvNets
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    Chapter 18 Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRI
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    Chapter 19 A Graph Based Similarity Measure for Assessing Altered Connectivity in Traumatic Brain Injury
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    Chapter 20 Multi-scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation
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    Chapter 21 Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network
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    Chapter 22 3D Texture Feature Learning for Noninvasive Estimation of Gliomas Pathological Subtype
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    Chapter 23 Pathology Segmentation Using Distributional Differences to Images of Healthy Origin
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    Chapter 24 Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns
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    Chapter 25 Stroke Lesion Segmentation with 2D Novel CNN Pipeline and Novel Loss Function
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    Chapter 26 Contra-Lateral Information CNN for Core Lesion Segmentation Based on Native CTP in Acute Stroke
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    Chapter 27 Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities
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    Chapter 28 Multi-scale Deep Convolutional Neural Network for Stroke Lesions Segmentation on CT Images
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    Chapter 29 Ischemic Stroke Lesion Segmentation Using Adversarial Learning
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    Chapter 30 V-Net and U-Net for Ischemic Stroke Lesion Segmentation in a Small Dataset of Perfusion Data
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    Chapter 31 Integrated Extractor, Generator and Segmentor for Ischemic Stroke Lesion Segmentation
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    Chapter 32 ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation
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    Chapter 33 Fully Automatic Segmentation for Ischemic Stroke Using CT Perfusion Maps
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    Chapter 34 Combining Good Old Random Forest and DeepLabv3+ for ISLES 2018 CT-Based Stroke Segmentation
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    Chapter 35 Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation
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    Chapter 36 Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal Loss
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    Chapter 37 MixNet: Multi-modality Mix Network for Brain Segmentation
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    Chapter 38 A Skip-Connected 3D DenseNet Networks with Adversarial Training for Volumetric Segmentation
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    Chapter 39 Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net
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    Chapter 40 3D Patchwise U-Net with Transition Layers for MR Brain Segmentation
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    Chapter 41 Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data
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    Chapter 42 A Combined Radio-Histological Approach for Classification of Low Grade Gliomas
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    Chapter 43 Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN
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    Chapter 44 Perfusion Parameter Estimation Using Neural Networks and Data Augmentation
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    Chapter 45 Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
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    Chapter 46 ICHNet: Intracerebral Hemorrhage (ICH) Segmentation Using Deep Learning
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    Chapter 47 Can Diffusion MRI Reveal Stroke-Induced Microstructural Changes in GM?