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 Wang^1,3, 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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