Automatic future remnant segmentation in liver resection planning.

Purpose: Liver resection is a complex procedure requiring precise removal of tumors while preserving viable tissue. This study proposes a novel approach for automated liver resection planning, using segmentations of the liver, vessels, and tumors from CT scans to predict the future liver remnant (FLR), aiming to improve pre-operative planning accuracy and patient outcomes.

Methods: This study evaluates deep convolutional and Transformer-based networks under various computational setups. Using different combinations of anatomical and pathological delineation masks, we assess the contribution of each structure. The method is initially tested with ground-truth masks for feasibility and later validated with predicted masks from a deep learning model.

Results: The experimental results highlight the crucial importance of incorporating anatomical and pathological masks for accurate FLR delineation. Among the tested configurations, the best performing model achieves an average Dice score of approximately 0.86, aligning closely with the inter-observer variability reported in the literature. Additionally, the model achieves an average symmetric surface distance of 0.95 mm, demonstrating its precision in capturing fine-grained structural details critical for pre-operative planning.

Conclusion: This study highlights the potential for fully-automated FLR segmentation pipelines in liver pre-operative planning. Our approach holds promise for developing a solution to reduce the time and variability associated with manual delineation. Such method can provide better decision-making in liver resection planning by providing accurate and consistent segmentation results. Future studies should explore its seamless integration into clinical workflows.