ArticleComputer methods and programs in biomedicine2017
A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.
Article in Computer methods and programs in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Correlation of imaging and plasma based biomarkers to predict response to bevacizumab in epithelial ovarian cancer (EOC).Gynecologic oncology · 2021Trial
- Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging.Journal of imaging informatics in medicine · 2025Article
- Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- US & MR/CT Image Fusion with Markerless Skin Registration: A Proof of Concept.Journal of imaging informatics in medicine · 2025Article
- BioCompNet: A Deep Learning Workflow Enabling Automated Body Composition Analysis toward Precision Management of Cardiometabolic Disorders.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts.Scientific reports · 2024Article
- Study of obesity research using machine learning methods: A bibliometric and visualization analysis from 2004 to 2023.Medicine · 2024Article
- Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Automated segmentation of five different body tissues on computed tomography using deep learning.Medical physics · 2023Article
- Estimating 3-D whole-body composition from a chest CT scan.Medical physics · 2022Article
- Deep learning for abdominal adipose tissue segmentation with few labelled samples.International journal of computer assisted radiology and surgery · 2022Article
- Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography.Scientific reports · 2021Article
- Deep Learning-based Quantification of Abdominal Subcutaneous and Visceral Fat Volume on CT Images.Academic radiology · 2021Article
- Artificial intelligence and abdominal adipose tissue analysis: a literature review.Quantitative imaging in medicine and surgery · 2021Review
- Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children.Radiology. Artificial intelligence · 2021Article
- Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.Sensors (Basel, Switzerland) · 2021Article
- Article
- Automated body composition analysis of clinically acquired computed tomography scans using neural networks.Clinical nutrition (Edinburgh, Scotland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups. The proposed CAD framework consisted of two steps with two convolution neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. The first CNN was trained using 2,240 CT slices to select abdominal CT slices depicting SFA and VFA. The second CNN was trained with 84,000pixel patches and applied to the selected CT slices to identify fat-related pixels and assign them into SFA and VFA classes. Comparing to the manual CT slice selection and fat pixel segmentation results, the accuracy of CT slice selection using the Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using the Segmentation-CNN was 96.8%. This study demonstrated the feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results.
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