Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray
This work addresses the need for automated FAI assessment in clinical MRI workflows, though it is incremental as it applies existing heatmap regression methods to a new modality.
The study tackled the problem of assessing FemoroAcetabular Impingement (FAI) by validating automated landmark detection across MRI and X-ray modalities in a matched-cohort of 89 patients, showing that MRI achieves equivalent localization and diagnostic accuracy to X-rays for cam-type impingement.
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