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

Overview of attention for book
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Springer International Publishing

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 12: Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI
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Chapter title
Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI
Chapter number 12
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_12
Book ISBNs
978-3-31-930857-9, 978-3-31-930858-6
Authors

Sérgio Pereira, Adriano Pinto, Victor Alves, Carlos A. Silva, Pereira, Sérgio, Pinto, Adriano, Alves, Victor, Silva, Carlos A.

Editors

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

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 20%
Student > Ph. D. Student 11 16%
Researcher 4 6%
Other 3 4%
Student > Bachelor 3 4%
Other 14 20%
Unknown 20 29%
Readers by discipline Count As %
Computer Science 21 30%
Engineering 14 20%
Neuroscience 3 4%
Unspecified 2 3%
Mathematics 1 1%
Other 4 6%
Unknown 24 35%
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 March 2018.
All research outputs
#18,456,836
of 22,869,263 outputs
Outputs from Lecture notes in computer science
#6,012
of 8,127 outputs
Outputs of similar age
#284,502
of 393,652 outputs
Outputs of similar age from Lecture notes in computer science
#446
of 581 outputs
Altmetric has tracked 22,869,263 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,652 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
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 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.