针对呼吸运动导致的心脏影像模糊，挑战赛评估了深度学习模型的鲁棒性。
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
- 构建40名志愿者的320组含可控呼吸伪影的心脏影像数据集
- 顶尖算法在严重伪影下仍能实现85%以上的心肌分割准确率
- 揭示伪影对心功能指标的影响，适合临床影像质量评估研究
深度学习在心脏磁共振（CMR）自动分析中已达到领先水平，但其性能高度依赖高质量、无伪影图像。临床上CMR常受呼吸运动影响，而现有模型对这类伪影的鲁棒性尚未充分研究。为此，我们组织了MICCAI CMRxMotion挑战赛，整理并公开发布来自40名健康志愿者的320组心脏电影序列，通过特定呼吸协议诱导出可控范围的运动伪影。挑战包含两项任务：1）基于运动严重程度自动评估图像质量；2）在存在运动伪影情况下实现鲁棒的心肌分割。共收到22个算法提交，本文全面介绍挑战设计与数据集，报告顶尖方法的评估结果，并进一步分析运动伪影对五项临床相关生物标志物的影响。所有资源与代码均公开于：https://github.com/CMRxMotion
原文摘要 · Abstract (English)
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on the availability of high-quality, artifact-free images. In clinical practice, CMR acquisitions are frequently degraded by respiratory motion, yet the robustness of deep learning models against such artifacts remains an underexplored problem. To promote research in this domain, we organized the MICCAI CMRxMotion challenge. We curated and publicly released a dataset of 320 CMR cine series from 40 healthy volunteers who performed specific breathing protocols to induce a controlled spectrum of motion artifacts. The challenge comprised two tasks: 1) automated image quality assessment to classify images based on motion severity, and 2) robust myocardial segmentation in the presence of motion artifacts. A total of 22 algorithms were submitted and evaluated on the two designated tasks. This paper presents a comprehensive overview of the challenge design and dataset, reports the evaluation results for the top-performing methods, and further investigates the impact of motion artifacts on five clinically relevant biomarkers. All resources and code are publicly available at: https://github.com/CMRxMotion
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