ArticleAcademic radiology2021
Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images.
Article in Academic radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Evolving perspectives on evaluating obesity: from traditional methods to cutting-edge techniques.Annals of medicine · 2025Review
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Improved muscle and fat segmentation for body composition measures on quantitative CT.International journal of computer assisted radiology and surgery · 2025Article
- Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis.BMC medical imaging · 2025Article
- Adiposity modifies the association between heart failure risk and glucose metabolic disorder in older individuals: a community-based prospective cohort study.Cardiovascular diabetology · 2024Article
- Female-specific pancreatic cancer survival from CT imaging of visceral fat implicates glutathione metabolism in solid tumors.Academic radiology · 2024Article
- SAROS: A dataset for whole-body region and organ segmentation in CT imaging.Scientific data · 2024Article
- Improved subcutaneous edema segmentation on abdominal CT using a generated adipose tissue density prior.International journal of computer assisted radiology and surgery · 2024Article
- Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition.Medical image analysis · 2024Article
- Current approach to the diagnosis of sarcopenia in cardiovascular diseases.Frontiers in nutrition · 2024Review
- Development of Multiscale 3D Residual U-Net to Segment Edematous Adipose Tissue by Leveraging Annotations from Non-Edematous Adipose Tissue.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.BMC bioinformatics · 2023Article
- Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.Tomography (Ann Arbor, Mich.) · 2023Article
- Determination of lower radiation dose limit for automatic measurement of adipose tissue.Journal of applied clinical medical physics · 2023Article
- Automated volume measurement of abdominal adipose tissue from entire abdominal cavity in Dixon MR images using deep learning.Radiological physics and technology · 2023Article
- Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS).Frontiers in physiology · 2023Article
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9 authors.
Funding
Abstract
RATIONALE AND
objectivesDevelop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images. MATERIALS AND
methodsSequential CT images spanning the abdominal region of seven subjects were manually segmented to calculate subcutaneous fat (SAT) and visceral fat (VAT). The resulting segmentation maps of SAT and VAT were augmented using a template-based data augmentation approach to create a large dataset for neural network training. Neural network performance was evaluated on both sequential CT slices from three subjects and randomly selected CT images from the upper, central, and lower abdominal regions of 100 subjects.
resultsBoth subcutaneous and abdominal cavity segmentation images created by the two methods were highly comparable with an overall Dice similarity coefficient of 0.94. Pearson's correlation coefficients between the subcutaneous and visceral fat volumes quantified using the two methods were 0.99 and 0.99 and the overall percent residual squared error were 5.5% and 8.5%. Manual segmentation of SAT and VAT on the 555 CT slices used for testing took approximately 46 hours while automated segmentation took approximately 1 minute.
conclusionOur data demonstrates that deep learning methods utilizing a template-based data augmentation strategy can be employed to accurately and rapidly quantify total abdominal SAT and VAT with a small number of training images.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.