首个公开的肝残余组织分割基准，助力结直肠肝转移手术规划
Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis
- 构建了197例标注完整的公开数据集，实现高精度肝残余分割
- 级联方法在肝残余分割上达0.767 Dice，预训练STU-Net对肿瘤分割更鲁棒
- 提供可复现框架，推动人工智能辅助外科手术研究
准确分割未来肝残余（FLR）对结直肠肝转移（CRLM）手术规划至关重要，可预防术后肝衰竭。但该任务因复杂的切除边界、迂曲的肝血管结构及弥散性转移灶而极具挑战性。现有自动化AI工具发展受限于缺乏高质量、经验证的数据。本文通过人工精修公开的CRLM-CT-Seg数据集中全部197个病例，创建首个开源且经验证的该任务基准。进一步建立分割基线，比较基于nnU-Net、SwinUNETR和STU-Net的级联（肝→肿瘤→肝残余）与端到端（E2E）策略。结果表明，级联nnU-Net在最终肝残余分割上取得0.767 Dice，而预训练的STU-Net在肿瘤分割上表现更优（0.620 Dice），且对级联误差具有更强鲁棒性。本工作首次提供经验证的基准与可复现框架，加速人工智能辅助手术规划研究。
原文摘要 · Abstract (English)
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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