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

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Table of Contents

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    Book Overview
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    Chapter 1 Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge
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    Chapter 2 Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Nets
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    Chapter 3 Automatic Brain Tumor Segmentation by Exploring the Multi-modality Complementary Information and Cascaded 3D Lightweight CNNs
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    Chapter 4 Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes
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    Chapter 5 Multimodal Brain Tumor Segmentation Using Cascaded V-Nets
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    Chapter 6 Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentation
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    Chapter 7 Extending 2D Deep Learning Architectures to 3D Image Segmentation Problems
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    Chapter 8 Tumor Segmentation and Survival Prediction in Glioma with Deep Learning
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    Chapter 9 Multi-planar Spatial-ConvNet for Segmentation and Survival Prediction in Brain Cancer
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    Chapter 10 A Pretrained DenseNet Encoder for Brain Tumor Segmentation
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    Chapter 11 Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Function
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    Chapter 12 Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
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    Chapter 13 Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinical Information
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    Chapter 14 Segmentation of Brain Tumors Using DeepLabv3+
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    Chapter 15 Brain Tumor Segmentation on Multimodal MR Imaging Using Multi-level Upsampling in Decoder
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    Chapter 16 Neuromorphic Neural Network for Multimodal Brain Image Segmentation and Overall Survival Analysis
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    Chapter 17 Glioma Segmentation with Cascaded UNet
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    Chapter 18 Segmentation of Gliomas and Prediction of Patient Overall Survival: A Simple and Fast Procedure
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    Chapter 19 Brain Tumour Segmentation Method Based on Supervoxels and Sparse Dictionaries
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    Chapter 20 Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation
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    Chapter 21 No New-Net
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    Chapter 22 3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation
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    Chapter 23 3D U-Net for Brain Tumour Segmentation
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    Chapter 24 Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training
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    Chapter 25 Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features
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    Chapter 26 A Novel Domain Adaptation Framework for Medical Image Segmentation
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    Chapter 27 Context Aware 3D CNNs for Brain Tumor Segmentation
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    Chapter 28 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization
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    Chapter 29 voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation
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    Chapter 30 Brain Tumor Segmentation and Survival Prediction Using a Cascade of Random Forests
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    Chapter 31 Automatic Segmentation of Brain Tumor Using 3D SE-Inception Networks with Residual Connections
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    Chapter 32 S3D-UNet: Separable 3D U-Net for Brain Tumor Segmentation
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    Chapter 33 Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas
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    Chapter 34 Automatic Brain Tumor Segmentation with Domain Adaptation
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    Chapter 35 Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation
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    Chapter 36 Automatic Brain Tumor Segmentation and Overall Survival Prediction Using Machine Learning Algorithms
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    Chapter 37 Deep Hourglass for Brain Tumor Segmentation
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    Chapter 38 Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction
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    Chapter 39 Semi-automatic Brain Tumor Segmentation by Drawing Long Axes on Multi-plane Reformat
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    Chapter 40 Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation
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    Chapter 41 Brain Tumor Segmentation Using Bit-plane and UNET
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    Chapter 42 Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction
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    Chapter 43 Ensemble of Fully Convolutional Neural Network for Brain Tumor Segmentation from Magnetic Resonance Images
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    Chapter 44 Learning Contextual and Attentive Information for Brain Tumor Segmentation
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    Chapter 45 Glioblastoma Survival Prediction
Attention for Chapter 38: Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction
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Chapter title
Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction
Chapter number 38
Book title
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-11726-9_38
Book ISBNs
978-3-03-011725-2, 978-3-03-011726-9
Authors

Yannick Suter, Alain Jungo, Michael Rebsamen, Urspeter Knecht, Evelyn Herrmann, Roland Wiest, Mauricio Reyes

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Student > Master 8 17%
Researcher 6 13%
Student > Postgraduate 4 9%
Lecturer 3 7%
Other 3 7%
Unknown 11 24%
Readers by discipline Count As %
Computer Science 16 35%
Medicine and Dentistry 5 11%
Engineering 4 9%
Mathematics 2 4%
Agricultural and Biological Sciences 2 4%
Other 4 9%
Unknown 13 28%