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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

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation
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    Chapter 2 Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials
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    Chapter 3 Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Units
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    Chapter 4 Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation
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    Chapter 5 MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection
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    Chapter 6 Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
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    Chapter 7 Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework
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    Chapter 8 Traumatic Brain Lesion Quantification Based on Mean Diffusivity Changes
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    Chapter 9 Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries
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    Chapter 10 Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation
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    Chapter 11 Brain Tumor Segmentation Using an Adversarial Network
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    Chapter 12 Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma
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    Chapter 13 Deep Learning Based Multimodal Brain Tumor Diagnosis
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    Chapter 14 Multimodal Brain Tumor Segmentation Using Ensemble of Forest Method
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    Chapter 15 Pooling-Free Fully Convolutional Networks with Dense Skip Connections for Semantic Segmentation, with Application to Brain Tumor Segmentation
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    Chapter 16 Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks
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    Chapter 17 3D Brain Tumor Segmentation Through Integrating Multiple 2D FCNNs
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    Chapter 18 MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks
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    Chapter 19 Automatic Segmentation and Overall Survival Prediction in Gliomas Using Fully Convolutional Neural Network and Texture Analysis
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    Chapter 20 Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks
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    Chapter 21 A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
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    Chapter 22 Dilated Convolutions for Brain Tumor Segmentation in MRI Scans
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    Chapter 23 Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation
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    Chapter 24 TPCNN: Two-Phase Patch-Based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction
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    Chapter 25 Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
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    Chapter 26 Multi-modal PixelNet for Brain Tumor Segmentation
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    Chapter 27 Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
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    Chapter 28 Brain Tumor Segmentation in MRI Scans Using Deeply-Supervised Neural Networks
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    Chapter 29 Brain Tumor Segmentation and Parsing on MRIs Using Multiresolution Neural Networks
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    Chapter 30 Brain Tumor Segmentation Using Deep Fully Convolutional Neural Networks
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    Chapter 31 Glioblastoma and Survival Prediction
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    Chapter 32 MRI Augmentation via Elastic Registration for Brain Lesions Segmentation
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    Chapter 33 Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation
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    Chapter 34 Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss
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    Chapter 35 Brain Tumor Segmentation Using a Multi-path CNN Based Method
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    Chapter 36 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences
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    Chapter 37 Automated Brain Tumor Segmentation on Magnetic Resonance Images and Patient’s Overall Survival Prediction Using Support Vector Machines
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    Chapter 38 Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
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    Chapter 39 Tumor Segmentation from Multimodal MRI Using Random Forest with Superpixel and Tensor Based Feature Extraction
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    Chapter 40 Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction
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    Chapter 41 WMH Segmentation Challenge: A Texture-Based Classification Approach
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    Chapter 42 White Matter Hyperintensities Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning
Attention for Chapter 6: Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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10 X users
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3 Facebook pages

Citations

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44 Dimensions

Readers on

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235 Mendeley
Summary X Facebook Dimensions citations
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Chapter title
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
Chapter number 6
Book title
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Published in
arXiv, September 2017
DOI 10.1007/978-3-319-75238-9_6
Book ISBNs
978-3-31-975237-2, 978-3-31-975238-9
Authors

Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera, Jinendra Ekanayake, Neil Kitchen, Sebastien Ourselin, Tom Vercauteren, Sébastien Ourselin, Fidon, Lucas, Li, Wenqi, Garcia-Peraza-Herrera, Luis C., Ekanayake, Jinendra, Kitchen, Neil, Ourselin, Sébastien, Vercauteren, Tom

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 235 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 235 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 53 23%
Student > Ph. D. Student 40 17%
Researcher 31 13%
Student > Bachelor 21 9%
Other 14 6%
Other 21 9%
Unknown 55 23%
Readers by discipline Count As %
Computer Science 85 36%
Engineering 50 21%
Medicine and Dentistry 5 2%
Mathematics 5 2%
Agricultural and Biological Sciences 4 2%
Other 13 6%
Unknown 73 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 08 November 2017.
All research outputs
#5,374,479
of 25,837,817 outputs
Outputs from arXiv
#92,985
of 937,964 outputs
Outputs of similar age
#83,612
of 326,889 outputs
Outputs of similar age from arXiv
#1,538
of 16,563 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 937,964 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 90% of its peers.
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 326,889 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 16,563 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.

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