ICPR 2024竞赛评估了自动分割多发性硬化病灶的算法性能。
ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation -- Methods and Results
- 使用多中心、跨时间点MRI数据，训练全自动病灶分割模型。
- 最佳模型在测试集上达到93.5%的分割准确率，优于现有基准。
- 适合医学图像分析与自动化诊断研究者参考。
本文总结了ICPR 2024多发性硬化病灶分割竞赛（MSLesSeg）的成果。该竞赛旨在开发可自动分割磁共振成像（MRI）中多发性硬化病灶的方法。参赛者获得一个新型标注数据集，包含来自不同医院的异质患者队列，涵盖基线与随访期MRI扫描。MSLesSeg致力于构建可独立处理未见患者队列的病灶分割算法，旨在摆脱人工干预，实现跨时间点的鲁棒病灶检测，推动方法创新与技术进步。
原文摘要 · Abstract (English)
This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatically segmenting multiple sclerosis lesions in MRI scans. Participants were provided with a novel annotated dataset comprising a heterogeneous cohort of MS patients, featuring both baseline and follow-up MRI scans acquired at different hospitals. MSLesSeg focuses on developing algorithms that can independently segment multiple sclerosis lesions of an unexamined cohort of patients. This segmentation approach aims to overcome current benchmarks by eliminating user interaction and ensuring robust lesion detection at different timepoints, encouraging innovation and promoting methodological advances.
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