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

arXiv: https://arxiv.org/abs/2601.18555 · HTML: https://mlsanity.com/paper/2601.18555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?](https://mlsanity.com/paper/2403.01470/) (2024)
- [Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images](https://mlsanity.com/paper/2407.18125/) (2024)

## Built on by (most cited first, 1 of 1)

- [CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection](https://mlsanity.com/paper/2606.04898/) (2026)

## Abstract (arXiv)

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