Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915756250234880 |
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| author | Di Via, Roberto Pastore, Vito Paolo Odone, Francesca Glyn-Jones, Siôn Voiculescu, Irina |
| author_facet | Di Via, Roberto Pastore, Vito Paolo Odone, Francesca Glyn-Jones, Siôn Voiculescu, Irina |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18555 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray Di Via, Roberto Pastore, Vito Paolo Odone, Francesca Glyn-Jones, Siôn Voiculescu, Irina Computer Vision and Pattern Recognition 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 |
| title | Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.18555 |