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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 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 12: Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
12 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
56 Mendeley
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Chapter title
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
Chapter number 12
Book title
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Published in
arXiv, September 2018
DOI 10.1007/978-3-030-11726-9_12
Book ISBNs
978-3-03-011725-2, 978-3-03-011726-9
Authors

Po-Yu Kao, Thuyen Ngo, Angela Zhang, Jefferson Chen, B. S. Manjunath, Jefferson W. Chen

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 27%
Student > Doctoral Student 5 9%
Student > Bachelor 4 7%
Student > Master 4 7%
Researcher 3 5%
Other 6 11%
Unknown 19 34%
Readers by discipline Count As %
Computer Science 24 43%
Engineering 8 14%
Medicine and Dentistry 1 2%
Neuroscience 1 2%
Unknown 22 39%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 15 November 2018.
All research outputs
#4,360,941
of 16,649,395 outputs
Outputs from arXiv
#88,490
of 666,429 outputs
Outputs of similar age
#89,519
of 281,667 outputs
Outputs of similar age from arXiv
#3,608
of 23,134 outputs
Altmetric has tracked 16,649,395 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 666,429 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 86% 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 281,667 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 68% of its contemporaries.
We're also able to compare this research output to 23,134 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.