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arXiv · 2604.07999

Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis

Anthony T. Wu·Arghavan Rezvani·Kela Liu·Roozbeh Houshyar·Pooya Khosravi·Whitney Li·Xiaohui Xie
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Abstract

Accurate segmentation of the future liver remnant (FLR) is critical for surgical planning in colorectal liver metastases (CRLM) to prevent fatal post-hepatectomy liver failure. However, this segmentation task is technically challenging due to complex resection boundaries, convoluted hepatic vasculature and diffuse metastatic lesions. A primary bottleneck in developing automated AI tools has been the lack of high-fidelity, validated data. We address this gap by manually refining all 197 volumes from the public CRLM-CT-Seg dataset, creating the first open-source, validated benchmark for this task. We then establish the first segmentation baselines, comparing cascaded (Liver->CRLM->FLR) and end-to-end (E2E) strategies using nnU-Net, SwinUNETR, and STU-Net. We find a cascaded nnU-Net achieves the best final FLR segmentation Dice (0.767), while the pretrained STU-Net provides superior CRLM segmentation (0.620 Dice) and is significantly more robust to cascaded errors. This work provides the first validated benchmark and a reproducible framework to accelerate research in AI-assisted surgical planning.

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Anthony T. Wu, Arghavan Rezvani, Kela Liu, Roozbeh Houshyar, Pooya Khosravi, Whitney Li, Xiaohui Xie. 2026-04-09. Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis. https://doi.org/10.1109/isbi61048.2026.11515981

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