Deep learning-based comprehensive multi-sequence liver lesion segmentation for accurate tumor burden assessment

Motivation: Accurate lesion segmentation is crucial for tumor burden assessment and subsequent patient-specific treatment prediction. Goal(s): To develop and validate a deep learning-based automated segmentation model for accurate hepatocellular carcinoma (HCC) lesion detection across various imaging sequences. Approach: A total of 2800 patients with focal liver lesions (FLLs) were included for developing automated segmentation models. The automated segmentation models were trained involving preprocessing, lesion detection using mask R-CNN, and lesion segmentation using a 3D-UNet framework. Results: The 3D-UNet framework is used for lesion segmentation, achieving a DSC accuracy ranging from 78.23% to 85.14% and volume ratios from 0.92 to 1.51 across different sequences. Impact: The model demonstrates promising potential in accurately segmenting HCC lesions.