ArticleSensors (Basel, Switzerland)2021
Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05496777 (Feasibility of Supervised Home-based Exercise Prehabilitation in Patients Scheduled for Pancreatic Resection), which is not on this map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Feasibility of Supervised Home-based Exercise Prehabilitation in Patients Scheduled for Pancreatic Resection
Who cites it
25 citing papers in PubMed.
- Association between low skeletal muscle mass assessed by deep learning-based CT and MASLD: a prospective cohort study.Diabetology & metabolic syndrome · 2026Article
- 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-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.Journal of imaging informatics in medicine · 2026Article
- Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models.Scientific reports · 2026Article
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Impact of abdominal adipose tissue segmentation on radiomics-based prognostic evaluation using CT enterography in Crohn's disease.BMC medical imaging · 2025Article
- Article
- Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging.Journal of imaging informatics in medicine · 2025Article
- Analysis of the current status of computed tomography diagnosis of sarcopenia.Archives of medical science : AMS · 2025Article
- Automated segmentation of trunk musculature with a deep CNN trained from sparse annotations in radiation therapy patients with metastatic spine disease: an observational study.Frontiers in bioengineering and biotechnology · 2025Article
- External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.The British journal of radiology · 2024Article
- Technologies and Sensors for Artificial Muscles in Rehabilitation.Sensors (Basel, Switzerland) · 2024Review
- Accelerated muscle mass estimation from CT images through transfer learning.BMC medical imaging · 2024Article
- Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.Tomography (Ann Arbor, Mich.) · 2024Article
- Article
- Supervised Home-Based Exercise Prehabilitation in Unfit Patients Scheduled for Pancreatic Surgery: Protocol for a Multicenter Feasibility Study.JMIR research protocols · 2023Article
- Determination of lower radiation dose limit for automatic measurement of adipose tissue.Journal of applied clinical medical physics · 2023Article
- A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.Quantitative imaging in medicine and surgery · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.Frontiers in nuclear medicine · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.
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Registered trials
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.