TriALS：非对比 CT 中的三相辅助肝脏病灶分割基准
计算机视觉与模式识别
2026-05-19 v1
摘要
非对比 CT 上肝脏病灶的自动分割在临床中具有重要意义，但在非对比剂量的非洲和亚洲等资源有限地区尤为具挑战性，其中经常无法使用造影剂。由于缺乏标注的非对比 CT 基准数据，进展有限。本文描述了 TriALS 挑战，用于在受限造影条件下进行肝脏病灶分割，支持来自埃及和中国机构的 150 例病例（600 个体积），采用四相 CT 扫描。算法在来自三家机构的 70 例（包括独立外部队列）上进行评估。最佳方法在 venous 相位获得平均 Dice 系数为 0.754，表现与人类水平相当，但在非对比 CT 上下降至 0.57。在外部验证中，领先方法在非对比 CT 上的 Dice 系数较现成模型提高了最高 28%。算法性能最强烈的预测因素为训练数据规模和预训练策略。跨年份比较揭示，非对比 CT 上仍存在顾虑性障碍，仅靠规模化预训练无法克服。数据、标注和代码均可在 https://github.com/xmed-lab/TriALS 获取。
引用
@article{arxiv.2605.16572,
title = {TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT},
author = {Marawan Elbatel and Mohamed Ghonim and Jiaji Mao and Zhuosheng Lin and Katharina Eckstein and Andrés Martínez Mora and Jonathan Deissler and Maximilian Rokuss and Constantin Ulrich and Zdravko Marinov and Wenhui Deng and Baoxun Li and Huijun Hu and Jun Shen and Mohanad Ghonim and Khadiga Omar Nassar and Mariam Elbakry and Menna Dyab and Amr Muhammad Abdo Salem and Nouran Elghitany and Noha Elghitany and Yi Qin and Xuanqi Huang and Haonan Wang and Shao-Woo Yen and Ahmed Elghamry Saba and Salma Ahmad and Xinyan Fang and Jiahao Zhang and Xiaodi Wang and Xinghua Ma and Gongning Luo and Jessica C. Delmoral and João Manuel R. S. Tavares and Ankan Deria and Adinath Dukre and Yutong Xie and Imran Razzak and Dongwook Kim and Matthew Choi and Hanxiao Zhang and Minghui Zhang and Xin You and Abdul Qayyum and Steven A. Niederer and Moona Mazher and Rachika E. Hamadache and Ricardo Montoya-del-Angel and Robert Martí and Xavier Lladó and Toufiq Musah and Livingstone Eli Ayivor and Enrique Almar-Munoz and Agnes Mayr and Kaouther Mouheb and Esther E. Bron and Stefan Klein and Ahmed Abouelhoda and Amira Adel and Susan Adil Ali and Rainer Stiefelhagen and Klaus H. Maier-Hein and Fabian Isensee and Aya Yassin and Xiaomeng Li},
journal= {arXiv preprint arXiv:2605.16572},
year = {2026}
}
备注
TriALS challenge paper across MICCAI 2024 and 2025; data and code at https://github.com/xmed-lab/TriALS