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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 23: Automatic Ischemic Stroke Lesion Segmentation in Multi-spectral MRI Images Using Random Forests Classifier
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Chapter title
Automatic Ischemic Stroke Lesion Segmentation in Multi-spectral MRI Images Using Random Forests Classifier
Chapter number 23
Book title
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Published in
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-30858-6_23
Book ISBNs
978-3-31-930857-9, 978-3-31-930858-6
Authors

Qaiser Mahmood, A. Basit, Qaiser, Mahmood, Basit, A.

Editors

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

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 17%
Student > Ph. D. Student 2 17%
Other 1 8%
Student > Doctoral Student 1 8%
Student > Bachelor 1 8%
Other 2 17%
Unknown 3 25%
Readers by discipline Count As %
Computer Science 3 25%
Engineering 3 25%
Mathematics 1 8%
Psychology 1 8%
Arts and Humanities 1 8%
Other 0 0%
Unknown 3 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 May 2016.
All research outputs
#20,317,110
of 22,858,915 outputs
Outputs from Lecture notes in computer science
#6,989
of 8,127 outputs
Outputs of similar age
#330,677
of 393,637 outputs
Outputs of similar age from Lecture notes in computer science
#501
of 581 outputs
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So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 581 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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