International journal of computer assisted radiology and surgery · 2026
Resection zone prediction for parenchyma-sparing hepatectomy planning: a comparative study of three modeling paradigms.
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Rothert Janine, Rakshit Joy, Salz Judith L, Huettl Florentine, Ehses Viola, Huber Tobias, Lang Hauke, Saalfeld Sylvia, Hille Georg
Аннотация
PURPOSE: Parenchyma-sparing hepatectomy planning depends on accurate resection zones that preserve functional liver tissue without compromising oncological margins. This work investigates how different levels of anatomical and functional information complexity influence resection zone prediction for parenchyma-sparing surgical planning for primary liver cancer.
METHODS: We compare three modeling paradigms: a geometric distance-based approach, an explicit perfusion-based method using vascular anatomy, and a deep learning-based model built on the U-Net architecture. All methods operate on segmentation-derived representations of the liver, tumor, and vessels. Performance is evaluated using overlap- and distance-based metrics.
RESULTS: The distance-based model produces predictions with limited surface deviation (HD 95 33.89 mm) but lower overlap due to undersegmentation (DSC 58.18 %). The perfusion-based method achieves a favorable balance between overlap (DSC 67.41 %) and boundary accuracy (HD 95 37.92 mm) but tends to overestimate the predicted region due to strict binary perfusion assumptions. The deep learning model attains the highest overlap accuracy (DSC 76.31 %) while exhibiting larger distance errors (HD 95 65.21 mm), reflecting localized boundary inaccuracies.
CONCLUSION: None of the models is universally outperforming the other two for parenchyma-sparing resection planning. Deep learning shows strong predictive performance, particularly for larger resection volumes, while geometric and perfusion-based models offer greater interpretability and clinical controllability. The distance-based approach is well suited for maximal parenchyma-sparing resections, whereas perfusion-based modeling is advantageous for tumors near vessels with potential perfusion loss. Our results highlight variations due to different surgical strategies and can thus provide valuable guidance for individual patient's surgery planning.
METHODS: We compare three modeling paradigms: a geometric distance-based approach, an explicit perfusion-based method using vascular anatomy, and a deep learning-based model built on the U-Net architecture. All methods operate on segmentation-derived representations of the liver, tumor, and vessels. Performance is evaluated using overlap- and distance-based metrics.
RESULTS: The distance-based model produces predictions with limited surface deviation (HD 95 33.89 mm) but lower overlap due to undersegmentation (DSC 58.18 %). The perfusion-based method achieves a favorable balance between overlap (DSC 67.41 %) and boundary accuracy (HD 95 37.92 mm) but tends to overestimate the predicted region due to strict binary perfusion assumptions. The deep learning model attains the highest overlap accuracy (DSC 76.31 %) while exhibiting larger distance errors (HD 95 65.21 mm), reflecting localized boundary inaccuracies.
CONCLUSION: None of the models is universally outperforming the other two for parenchyma-sparing resection planning. Deep learning shows strong predictive performance, particularly for larger resection volumes, while geometric and perfusion-based models offer greater interpretability and clinical controllability. The distance-based approach is well suited for maximal parenchyma-sparing resections, whereas perfusion-based modeling is advantageous for tumors near vessels with potential perfusion loss. Our results highlight variations due to different surgical strategies and can thus provide valuable guidance for individual patient's surgery planning.