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Abstract #0537

Automatic Detection of Small Hepatocellular Carcinoma (≤2 cm) in Cirrhotic Liver based on Pattern Matching and Deep Learning

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

This study presented an algorithm for small hepatocellular carcinoma (sHCC) detection and segmentation in cirrhotic liver based on diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) images. The model included two-steps: screening of suspicious lesions in DWI using pattern matching algorithm; identification and segmentation of true lesions in DCE based on deep learning. The proposed model exhibited superior performance in sHCC (≤2 cm) detection and segmentation, which significantly outperformed the Liver Imaging Reporting and Data System (LI-RADS) based diagnosis.

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