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Autonomous localization and segmentation for body composition quantization on abdominal CT

https://doi.org/10.1016/j.bspc.2021.103172

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The evolution of body composition in oncology—epidemiology, clinical trials, and the future of patient care: facts and numbers

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ABCNet: A new efficient 3D dense‐structure network for segmentation and analysis of body tissue composition on body‐torso‐wide CT images

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Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

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Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema

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Body Fat Assessment Method Using CT Images with Separation Mask Algorithm

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Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis

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CT image denoising using NLM and its method noise thresholding

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Computer-aided Assessment of Regional Abdominal Fat with Food Residue Removal in CT

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A review on CT image noise and its denoising

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RescueNet: An unpaired GAN for brain tumor segmentation

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CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain

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Automated body composition analysis of clinically acquired computed tomography scans using neural networks

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A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

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Muscle segmentation in axial computed tomography (CT) images at the lumbar (L3) and thoracic (T4) levels for body composition analysis

inferredcites →https://doi.org/10.1016/j.compmedimag.2020.101776openalex-snapshot

Deep learning method for localization and segmentation of abdominal CT

inferredcites →https://doi.org/10.1016/j.compmedimag.2021.101929openalex-snapshot

Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images

inferredcites →https://doi.org/10.1016/j.ejrad.2020.109153openalex-snapshot

Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation

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Sarcopenia, intramuscular fat deposition, and visceral adiposity independently predict the outcomes of hepatocellular carcinoma

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Generative adversarial network in medical imaging: A review

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Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes

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Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies

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Correction: Corrigendum: Cochlear gene therapy with ancestral AAV in adult mice: complete transduction of inner hair cells without cochlear dysfunction

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Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study

inferredcites →https://doi.org/10.1097/rli.0000000000000647openalex-snapshot

Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis

inferredcites →https://doi.org/10.1109/3dv.2016.79openalex-snapshot

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

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2016 Fourth International Conference on 3D Vision (3DV)

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Automatic Segmentation of Abdominal Fat from CT Data

inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

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Deep Residual Learning for Image Recognition

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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Densely Connected Convolutional Networks

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Image-to-Image Translation with Conditional Adversarial Networks

inferredcites →https://doi.org/10.1109/iccke.2018.8566372openalex-snapshot

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE)

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Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation

inferredcites →https://doi.org/10.1109/iccv.1995.466933openalex-snapshot

Proceedings of IEEE International Conference on Computer Vision

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inferredcites →https://doi.org/10.1109/jbhi.2020.2986926openalex-snapshot

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Contrast Enhancement of Multiple Tissues in MR Brain Images With Reversibility

inferredcites →https://doi.org/10.1109/tmi.2015.2479252openalex-snapshot

Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle

inferredcites →https://doi.org/10.1109/tpami.2019.2913372openalex-snapshot

Squeeze-and-Excitation Networks

inferredcites →https://doi.org/10.1117/12.2007970openalex-snapshot

Segmenting the thoracic, abdominal and pelvic musculature on CT scans combining atlas-based model and active contour model

inferredcites →https://doi.org/10.1117/12.812412openalex-snapshot

Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis

inferredcites →https://doi.org/10.1117/12.878017openalex-snapshot

Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans

inferredcites →https://doi.org/10.1139/h08-075openalex-snapshot

A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care

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Proceedings of the Third International Symposium on Image Computing and Digital Medicine

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Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning

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Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image

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Development of Automatic Visceral Fat Volume Calculation Software for CT Volume Data

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Obesity and Cancer: The Role of Dysfunctional Adipose Tissue

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Cancer Cachexia in the Age of Obesity: Skeletal Muscle Depletion Is a Powerful Prognostic Factor, Independent of Body Mass Index

inferredcites →https://doi.org/10.1371/journal.pone.0183515openalex-snapshot

Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images

inferredcites →https://doi.org/10.3156/jsoft.29.5_177_2openalex-snapshot

GAN（Generative Adversarial Nets）

inferredcites →https://doi.org/10.3945/ajcn.113.071399openalex-snapshot

Obesity paradox in cancer: new insights provided by body composition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1411.1784openalex-snapshot

Conditional Generative Adversarial Nets

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Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

inferredcites →https://doi.org/10.48550/arxiv.1801.04406openalex-snapshot

Which Training Methods for GANs do actually Converge?

inferredcites →https://doi.org/10.48550/arxiv.1804.03999openalex-snapshot

Attention U-Net: Learning Where to Look for the Pancreas

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CBAM: Convolutional Block Attention Module

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Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net

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TensorFlow: A system for large-scale machine learning

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Ca-Segresnet: A Context-Aware Segmentation Residual Network for Automatic Segmentation of Identified Vertebral Bones from Ct Images

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Gender‐specific data‐driven adiposity subtypes using deep‐learning‐based abdominal CT segmentation

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Cascade connection-based channel attention network for bidirectional medical image registration

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Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection

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Comparative analysis of ResNet, ResNet-SE, and attention-based RaNet for hemorrhage classification in CT images using deep learning

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Self-supervised multi-organ segmentation for aging pattern exploration

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Simplified-attention Enhanced Graph Convolutional Network for 3D human pose estimation

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Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm

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