Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 144, Issue -, Pages 97-104Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2017.03.017
Keywords
Computer-aided detection (CAD); Deep learning; Convolution neural network (CNN); Segmentation of adipose tissue; Subcutaneous fat area (SFA); Visceral fat area (VFA)
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Funding
- National Cancer Institute, National Institutes of Health [R01 CA197150]
- Center for the Advancement of Science and Technology, State of Oklahoma [HR15-016]
- Peggy and Charles Stephenson Cancer Center, University of Oklahoma
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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 sub-cutaneous 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 84,000 pixel 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. (C) 2017 Elsevier B.V. All rights reserved.
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