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

TL;DR

This paper introduces the first validated open-source benchmark for liver remnant segmentation in colorectal liver metastasis, comparing multiple deep learning models and strategies.

Contribution

It creates a high-quality, validated dataset and establishes baseline models, including cascaded and end-to-end approaches, for future research in this domain.

Findings

Cascaded nnU-Net achieved the highest FLR segmentation Dice of 0.767.

Pretrained STU-Net provided better CRLM segmentation and robustness.

The benchmark accelerates AI research for surgical planning in liver metastasis.

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.