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.
@article{arxiv.2604.07999,
title = {Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis},
author = {Anthony T. Wu and Arghavan Rezvani and Kela Liu and Roozbeh Houshyar and Pooya Khosravi and Whitney Li and Xiaohui Xie},
journal= {arXiv preprint arXiv:2604.07999},
year = {2026}
}
Comments
Accepted at the 2026 International Symposium on Biomedical Imaging (ISBI) Oral 4-page paper presentation