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

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Cover of 'Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries'

Table of Contents

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    Book Overview
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    Chapter 1 Brain Lesions, Introduction
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    Chapter 2 Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models
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    Chapter 3 Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular Territories
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    Chapter 4 Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithms
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    Chapter 5 Combining Unsupervised and Supervised Methods for Lesion Segmentation
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    Chapter 6 Assessment of Tissue Injury in Severe Brain Trauma
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    Chapter 7 A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequences
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    Chapter 8 A Semi-automatic Method for Segmentation of Multiple Sclerosis Lesions on Dual-Echo Magnetic Resonance Images
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    Chapter 9 Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Images
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    Chapter 10 A Quantitative Approach to Characterize MR Contrasts with Histology
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    Chapter 11 Image Features for Brain Lesion Segmentation Using Random Forests
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    Chapter 12 Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI
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    Chapter 13 GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation
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    Chapter 14 Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors
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    Chapter 15 Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape
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    Chapter 16 Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders
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    Chapter 17 A Convolutional Neural Network Approach to Brain Tumor Segmentation
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    Chapter 18 ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual Clustering
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    Chapter 19 Stroke Lesion Segmentation of 3D Brain MRI Using Multiple Random Forests and 3D Registration
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    Chapter 20 Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level Set
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    Chapter 21 ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images
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    Chapter 22 A Voxel-Wise, Cascaded Classification Approach to Ischemic Stroke Lesion Segmentation
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    Chapter 23 Automatic Ischemic Stroke Lesion Segmentation in Multi-spectral MRI Images Using Random Forests Classifier
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    Chapter 24 Segmenting the Ischemic Penumbra: A Decision Forest Approach with Automatic Threshold Finding
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    Chapter 25 Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation
Attention for Chapter 16: Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders
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Chapter title
Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders
Chapter number 16
Book title
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Published by
Springer International Publishing, January 2016
DOI 10.1007/978-3-319-30858-6_16
Book ISBNs
978-3-31-930857-9, 978-3-31-930858-6
Authors

Kiran Vaidhya, Subramaniam Thirunavukkarasu, Varghese Alex, Ganapathy Krishnamurthi

Editors

Alessandro Crimi, Bjoern Menze, Oskar Maier, Mauricio Reyes, Heinz Handels

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 15%
Researcher 4 10%
Student > Master 3 7%
Professor 3 7%
Professor > Associate Professor 2 5%
Other 4 10%
Unknown 19 46%
Readers by discipline Count As %
Engineering 9 22%
Computer Science 6 15%
Agricultural and Biological Sciences 1 2%
Arts and Humanities 1 2%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 22 54%