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

Overview of attention for book
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
  3. Altmetric Badge
    Chapter 2 Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Nets
  4. Altmetric Badge
    Chapter 3 Automatic Brain Tumor Segmentation by Exploring the Multi-modality Complementary Information and Cascaded 3D Lightweight CNNs
  5. Altmetric Badge
    Chapter 4 Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes
  6. Altmetric Badge
    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
  8. Altmetric Badge
    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
  12. Altmetric Badge
    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
  14. Altmetric Badge
    Chapter 13 Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinical Information
  15. Altmetric Badge
    Chapter 14 Segmentation of Brain Tumors Using DeepLabv3+
  16. Altmetric Badge
    Chapter 15 Brain Tumor Segmentation on Multimodal MR Imaging Using Multi-level Upsampling in Decoder
  17. Altmetric Badge
    Chapter 16 Neuromorphic Neural Network for Multimodal Brain Image Segmentation and Overall Survival Analysis
  18. Altmetric Badge
    Chapter 17 Glioma Segmentation with Cascaded UNet
  19. Altmetric Badge
    Chapter 18 Segmentation of Gliomas and Prediction of Patient Overall Survival: A Simple and Fast Procedure
  20. Altmetric Badge
    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
  23. Altmetric Badge
    Chapter 22 3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation
  24. Altmetric Badge
    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
  26. Altmetric Badge
    Chapter 25 Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features
  27. Altmetric Badge
    Chapter 26 A Novel Domain Adaptation Framework for Medical Image Segmentation
  28. Altmetric Badge
    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
  31. Altmetric Badge
    Chapter 30 Brain Tumor Segmentation and Survival Prediction Using a Cascade of Random Forests
  32. Altmetric Badge
    Chapter 31 Automatic Segmentation of Brain Tumor Using 3D SE-Inception Networks with Residual Connections
  33. Altmetric Badge
    Chapter 32 S3D-UNet: Separable 3D U-Net for Brain Tumor Segmentation
  34. Altmetric Badge
    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
  35. Altmetric Badge
    Chapter 34 Automatic Brain Tumor Segmentation with Domain Adaptation
  36. Altmetric Badge
    Chapter 35 Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation
  37. Altmetric Badge
    Chapter 36 Automatic Brain Tumor Segmentation and Overall Survival Prediction Using Machine Learning Algorithms
  38. Altmetric Badge
    Chapter 37 Deep Hourglass for Brain Tumor Segmentation
  39. Altmetric Badge
    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
  41. Altmetric Badge
    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
  43. Altmetric Badge
    Chapter 42 Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction
  44. Altmetric Badge
    Chapter 43 Ensemble of Fully Convolutional Neural Network for Brain Tumor Segmentation from Magnetic Resonance Images
  45. Altmetric Badge
    Chapter 44 Learning Contextual and Attentive Information for Brain Tumor Segmentation
  46. Altmetric Badge
    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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
11 X users
patent
1 patent

Citations

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17 Dimensions

Readers on

mendeley
86 Mendeley
Summary X Patents Dimensions citations
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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, Kao, Po-Yu, Ngo, Thuyen, Zhang, Angela, Chen, Jefferson W., Manjunath, B. S.

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 21%
Student > Master 7 8%
Student > Bachelor 7 8%
Student > Doctoral Student 5 6%
Researcher 5 6%
Other 8 9%
Unknown 36 42%
Readers by discipline Count As %
Computer Science 30 35%
Engineering 11 13%
Unspecified 1 1%
Psychology 1 1%
Mathematics 1 1%
Other 2 2%
Unknown 40 47%
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 12 April 2022.
All research outputs
#5,125,028
of 24,998,746 outputs
Outputs from arXiv
#108,685
of 1,020,287 outputs
Outputs of similar age
#86,180
of 316,938 outputs
Outputs of similar age from arXiv
#2,671
of 22,077 outputs
Altmetric has tracked 24,998,746 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 1,020,287 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 89% 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 316,938 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 72% of its contemporaries.
We're also able to compare this research output to 22,077 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.

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