用AI自动检测髋关节影像中的关键点，验证MRI与X光结果一致。
Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray
- 基于热力图回归模型，在MRI中定位髋关节关键解剖点。
- 89例配对数据验证：MRI检测精度与传统X光相当。
- 适合骨科医生做髋关节发育不良筛查，支持自动化流程集成。
临床筛查常依赖角度测量，特别是股骨头-髋臼撞击症（FAI）的诊断主要基于X光片上的角度评估。但评估撞击区域的高度和范围需借助MRI提供的三维视角。本研究通过89名患者的配对MRI/X光数据，采用标准热力图回归架构开展跨模态验证，证实了在冠状面3D MRI上，自动化地标检测可实现与X光相当的定位精度和诊断准确性。该方法为在常规MRI流程中引入自动化FAI评估提供了可行性，未来可扩展至更多地标以支持体积化分析。代码已开源。
原文摘要 · Abstract (English)
Many clinical screening decisions are based on angle measurements. In particular, FemoroAcetabular Impingement (FAI) screening relies on angles traditionally measured on X-rays. However, assessing the height and span of the impingement area requires also a 3D view through an MRI scan. The two modalities inform the surgeon on different aspects of the condition. In this work, we conduct a matched-cohort validation study (89 patients, paired MRI/X-ray) using standard heatmap regression architectures to assess cross-modality clinical equivalence. Seen that landmark detection has been proven effective on X-rays, we show that MRI also achieves equivalent localisation and diagnostic accuracy for cam-type impingement. Our method demonstrates clinical feasibility for FAI assessment in coronal views of 3D MRI volumes, opening the possibility for volumetric analysis through placing further landmarks. These results support integrating automated FAI assessment into routine MRI workflows. Code is released at https://github.com/Malga-Vision/Landmarks-Hip-Conditions
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