Rencheng Zheng1, Luna Wang2, Chengyan Wang3, Xuchen Yu1, Weibo Chen4, Yan Li5, Weixia Li5, Fuhua Yan5, He Wang1,3, and Ruokun Li5
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Department of Radiology, Shanghai Chest Hospital, Shanghai, China, 3Human Phenome Institute, Fudan University, Shanghai, China, 4Market Solutions Center, Philips Healthcare, Shanghai, China, 5Department of Radiology, Ruijin Hospital, Shanghai, China
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Department of Radiology, Shanghai Chest Hospital, Shanghai, China, 3Human Phenome Institute, Fudan University, Shanghai, China, 4Market Solutions Center, Philips Healthcare, Shanghai, China, 5Department of Radiology, Ruijin Hospital, Shanghai, China
Feasibility of automatic detection and segmentation of small hepatocellular
carcinoma (≤2 cm) in cirrhotic liver based on diffusion-weighted imaging and dynamic
contrast-enhanced images with pattern matching and deep learning model.
Figure 1. Overall framework of the proposed PM-DL model, including a)
image registration and liver segmentation, b) screening of suspicious lesions
in DWI images, and c) identification and segmentation of true lesions in DCE
images.
Figure 4. Two representative sHCCs correctly detected by the PM-DL
model in the external test cohort, a) LR-4 according to LI-RADS v2018, which
can be regarded as HCCs, Edmondson–Steiner grade III; b) LR-M according to
LI-RADS v2018, which cannot be regarded as HCCs, Edmondson–Steiner grade II.
(Yellow box: extracted image patch, green contour: manually labeled mask,
yellow contour: model predicted mask).