Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916424718483456 |
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| author | Wyatt, Julian Voiculescu, Irina |
| author_facet | Wyatt, Julian Voiculescu, Irina |
| contents | Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation procedure. The challenge ranks our method's results as the best in MRE of 1.186mm and third in the 2mm SDR of 82.04% on the online validation leaderboard. The code is available at https://github.com/Julian-Wyatt/OptimisingfortheUnknown. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection Wyatt, Julian Voiculescu, Irina Computer Vision and Pattern Recognition Cephalometric Landmark Detection is the process of identifying key areas for cephalometry. Each landmark is a single GT point labelled by a clinician. A machine learning model predicts the probability locus of a landmark represented by a heatmap. This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact augmentation procedure. The challenge ranks our method's results as the best in MRE of 1.186mm and third in the 2mm SDR of 82.04% on the online validation leaderboard. The code is available at https://github.com/Julian-Wyatt/OptimisingfortheUnknown. |
| title | Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.04445 |