3 March 2017 Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images
Author Affiliations +
Abstract
Abdominal obesity is strongly associated with a number of diseases and accurately assessment of subtypes of adipose tissue volume plays a significant role in predicting disease risk, diagnosis and prognosis. The objective of this study is to develop and evaluate a new computer-aided detection (CAD) scheme based on deep learning models to automatically segment subcutaneous fat areas (SFA) and visceral (VFA) fat areas depicting on CT images. A dataset involving CT images from 40 patients were retrospectively collected and equally divided into two independent groups (i.e. training and testing group). The new CAD scheme consisted of two sequential convolutional neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. Selection-CNN was trained using 2,240 CT slices to automatically select CT slices belonging to abdomen areas and SegmentationCNN was trained using 84,000 fat-pixel patches to classify fat-pixels as belonging to SFA or VFA. Then, data from the testing group was used to evaluate the performance of the optimized CAD scheme. Comparing to manually labelled results, the classification accuracy of CT slices selection generated by Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using Segmentation-CNN yielded 96.8%. Therefore, this study demonstrated the feasibility of using deep learning based CAD scheme to recognize human abdominal section from CT scans and segment SFA and VFA from CT slices with high agreement compared with subjective segmentation results.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunzhi Wang, Yunzhi Wang, Yuchen Qiu, Yuchen Qiu, Theresa Thai, Theresa Thai, Kathleen Moore, Kathleen Moore, Hong Liu, Hong Liu, Bin Zheng, Bin Zheng, } "Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343G (3 March 2017); doi: 10.1117/12.2250360; https://doi.org/10.1117/12.2250360
PROCEEDINGS
6 PAGES


SHARE
Back to Top