A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images
Open accessAbstract
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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Deep learning-based algorithms have increasingly been investigated for automated segmentation of body composition from medical imaging.
“In recent years, deep learning-based algorithms have increasingly been investigated for automated segmentation of body composition from medical imaging [14, 15].”
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- Authors
- Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B.
- Affiliations
- University of Oklahoma (US)
- University of Oklahoma Health Sciences Center (US)
- Corresponding author
- Wang, Yunzhi
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 144
- Pages
- 97-104
- ISSN
- 0169-2607 (ISSN-L)
- PMC
- PMC5441239
- OpenAlex
- W2603403091
- Cited by
- 110
- References
- 39
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 9 · 2023: 3 · 2022: 19 · 2021: 17 · 2020: 22 · 2019: 16 · 2018: 11 · 2017: 6
- FWCI
- 6.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Stephenson Cancer Center
- National Cancer Institute (R01CA197150)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- computer-aided detection, adipose tissue segmentation, subcutaneous adipose tissue, visceral fat, CT images, volumetric CT, convolutional neural networks, abdominal CT
- MeSH terms
- Abdomen, Humans, Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, Neural Networks, Computer, Subcutaneous Fat, Intra-Abdominal Fat, Abdominal Fat
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