ArticleMagma (New York, N.Y.)2022
Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning.
Article in Magma (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Perirenal fat as a potential marker and therapeutic target for metabolic syndrome: insights from a multicenter randomized controlled trial.Frontiers in endocrinology · 2025Trial
- Impact of Visceral Obesity on Clinical Outcome and Quality of Life for Patients with Multiple Myeloma: A Secondary Data Analysis of STaMINA (BMT CTN 0702) Trial.Transplantation and cellular therapy · 2024Trial
- Adaptive deep learning for quantification and spatial distribution of abdominal adipose tissue from magnetic resonance imaging proton density fat fraction in adults with a body mass index ≥24 kg/mQuantitative imaging in medicine and surgery · 2026Article
- Assessment of MRI-Derived proton density fat fraction of total subcutaneous adipose tissue for discrimination primary aldosteronism subtypes.Endocrine · 2026Article
- Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation and quantification using free-breathing 3D MRI.Radiology advances · 2026Article
- Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.Quantitative imaging in medicine and surgery · 2025Article
- Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation.Radiology. Artificial intelligence · 2024Article
- Fat Fraction Extracted from Whole-Body Magnetic Resonance (WB-MR) in Bone Metastatic Prostate Cancer: Intra- and Inter-Reader Agreement of Single-Slice and Volumetric Measurements.Tomography (Ann Arbor, Mich.) · 2024Article
- Quantification of Visceral Fat at the L5 Vertebral Body Level in Patients with Crohn's Disease Using T2-Weighted MRI.Bioengineering (Basel, Switzerland) · 2024Article
- Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.BMC bioinformatics · 2023Article
- Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort.Science advances · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images. MATERIALS AND
methodsAdipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat-water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images).
resultsThe automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets.
conclusionThe proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.
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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.