Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray

TL;DR

This study validates that automated landmark detection on MRI is as accurate as on X-ray for assessing hip impingement, enabling comprehensive 3D analysis and routine clinical use.

Contribution

It demonstrates the clinical feasibility of automated landmark detection on MRI for hip assessment, extending previous X-ray methods to 3D volumetric analysis.

Findings

MRI achieves equivalent localization accuracy to X-ray for FAI landmarks.

Automated detection is feasible on coronal MRI views for clinical assessment.

Code for the method is publicly available.

Abstract

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