Keywords: Analysis/Processing, Liver
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.
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