ArticlePloS one2017
Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Role of systemic and epicardial adipose tissue in cardiometabolic disease.Nature reviews. Cardiology · 2026Review
- Optimizing CT-Based Models for Predicting Whole-Body Fat in Rabbits.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Deep Learning-Based Automatic Muscle Segmentation of the Thigh Using Lower Extremity CT Images.Diagnostics (Basel, Switzerland) · 2025Article
- Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration.Cancers · 2024Review
- Circulating cancer-associated macrophage-like cells and macrophage-related cytokines in obese patients with advanced breast cancer who undergo neoadjuvant chemotherapy.Journal of Cancer · 2024Article
- Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.Tomography (Ann Arbor, Mich.) · 2023Article
- Association between Birth Weight and Subcutaneous Fat Thickness at Adulthood in Dogs.Veterinary sciences · 2023Article
- Expected and paradoxical effects of obesity on cancer treatment response.Reviews in endocrine & metabolic disorders · 2021Review
- Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks.European radiology · 2021Article
- The Prognostic Impact of Body Composition for Locally Advanced Breast Cancer Patients Who Received Neoadjuvant Chemotherapy.Cancers · 2021Article
- Obesity and Breast Cancer: The Role of Crown-Like Structures in Breast Adipose Tissue in Tumor Progression, Prognosis, and Therapy.Journal of breast cancer · 2020Review
- Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.Skeletal radiology · 2020Article
- Dual-Energy X-Ray Absorptiometry Compared to Computed Tomography for Visceral Adiposity Assessment Among Gastrointestinal and Pancreatic Cancer Survivors.Scientific reports · 2019Article
- Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort.The British journal of radiology · 2018Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundVisceral adiposity is a risk factor for many chronic diseases. Existing methods to quantify visceral adipose tissue volume using computed tomographic (CT) images often use a single slice, are manual, and are time consuming, making them impractical for large population studies. We developed and validated a method to accurately, rapidly, and robustly measure visceral adipose tissue volume using CT images.
methodsIn-house software, Medical Executable for the Efficient and Robust Quantification of Adipose Tissue (MEERQAT), was developed to calculate visceral adipose tissue volume using a series of CT images within a manually identified region of interest. To distinguish visceral and subcutaneous adipose tissue, ellipses are drawn through the rectus abdominis and transverse abdominis using manual and automatic processes. Visceral and subcutaneous adipose tissue volumes are calculated by counting the numbers of voxels corresponding to adipose tissue in the region of interest. MEERQAT's ellipse interpolation method was validated by comparing visceral adipose volume from 10 patients' CT scans with corresponding results from manually delineated scans. Accuracy of visceral adipose quantification was tested using a phantom consisting of animal fat and tissues. Robustness of the method was tested by determining intra-observer and inter-observer coefficients of variation (CV).
resultsThe mean difference in visceral adipose tissue volume between manual and elliptical delineation methods was -0.54 ± 4.81%. In the phantom, our measurement differed from the known adipose volume by ≤ 7.5% for all scanning parameters. Mean inter-observer CV for visceral adipose tissue volume was 0.085, and mean intra-observer CV for visceral adipose tissue volume was 0.059.
conclusionsWe have developed and validated a robust method of accurately and quickly determining visceral adipose tissue volume in any defined region of interest using CT imaging.
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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 OpenQuestion graph.