ArticleEuropean radiology2021
Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 100 papers, 1 of them a synthesis that pooled it.
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Who cites it
100 citing papers in PubMed, 1 synthesis or guideline pooled it, 123 citations in OpenAlex.
- A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis.Journal of cachexia, sarcopenia and muscle · 2023Pooled it
- Predicting gallstones risk with body composition analysis and machine learning: a dual-center cohort study.Annals of medicine · 2026Article
- Epicardial and pericardial adipose tissue: anatomy, physiology, imaging, segmentation, and treatment effects.The British journal of radiology · 2026Review
- A Multi-Model, Pixel-Native Framework for Automated Computed Tomography Series Labeling and Characterization: Proof-of-Concept Study.JMIR medical informatics · 2026Article
- Technical feasibility of AI-based longitudinal multi-organ volumetry on low-dose PET/CT over 13 years.Japanese journal of radiology · 2026Article
- Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning.European radiology · 2026Article
- Prognostic value of body composition analysis in cancer patients treated with immunotherapy: a systematic evaluation of TotalSegmentator-based biomarkers.La Radiologia medica · 2026Article
- Article
- Automated L3 Skeletal Muscle Segmentation for the Evaluation of Sarcopenia: Development and Independent Validation of an Ensemble-Based 2D nnU-Net Pipeline in a Complex Liver Disease Cohort.Muscles (Basel, Switzerland) · 2026Article
- Deep learning-derived pericardial adipose tissue by electrocardiogram-gated cardiac computed tomography predicts cardiovascular events beyond coronary calcium score.American journal of preventive cardiology · 2026Article
- Osegnet-F-Unext: O-Segnet-Fusion-Unext for pulmonary lobe segmentation of Covid-19 using Computed Tomography image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Muscle and fat matter: Automated CT-based body composition analysis predicts survival inEuropean journal of radiology open · 2026Article
- Technical performance of L3 skeletal muscle area (SMA) measurement on CT for L3 skeletal muscle index (L3SMI) assessment.Scientific reports · 2026Article
- Systemic inflammation plays a key prognostic role in patients with head and neck cancer treated with immunotherapy and is linked to CT-based body composition metrics.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition-Based Deep Learning.Cancer medicine · 2026Article
- Thoracic myosteatosis and adipose tissue in COPD: associations of muscle quality and CT biomarkers with GOLD classification.BMC pulmonary medicine · 2026Observational
- Automated contrast-to-noise ratio analysis in chest CT: validation of an open-source segmentation approach.Insights into imaging · 2026Article
- Reducing manual workload in CT and MRI annotation with the Segment Anything Model 2.BMC medical imaging · 2026Article
- Artificial intelligence-driven body composition analysis and its association with survival in patients with colorectal liver metastases.Contemporary oncology (Poznan, Poland) · 2026Article
- Multimodal AI Model for Sarcopenia Detection: Integrating Chest CT and Clinical Parameters in Older Adults.Clinical interventions in aging · 2026Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesBody tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging.
methodsTherefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits.
resultsThe Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99.
conclusionsOur results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. KEY POINTS: • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.
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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.