A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916744056012800 |
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| author | Macias, Lorena Garcia-Foncillas Kujawa, Aaron Elshalakany, Aya Shapey, Jonathan Vercauteren, Tom |
| author_facet | Macias, Lorena Garcia-Foncillas Kujawa, Aaron Elshalakany, Aya Shapey, Jonathan Vercauteren, Tom |
| contents | Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplete defacing or degradation of brain tissue regions. We present a robust, generalisable defacing pipeline for high-resolution MRI that integrates atlas-based registration with brain masking. Our method was evaluated on 2,566 heterogeneous clinical scans for meningioma and achieved a 99.92 per cent success rate (2,564/2,566) upon visual inspection. Excellent anatomical preservation is demonstrated with a Dice similarity coefficient of 0.9975 plus or minus 0.0023 between brain masks automatically extracted from the original and defaced volumes. Source code is available at https://github.com/cai4cai/defacing_pipeline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans Macias, Lorena Garcia-Foncillas Kujawa, Aaron Elshalakany, Aya Shapey, Jonathan Vercauteren, Tom Image and Video Processing Computer Vision and Pattern Recognition Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplete defacing or degradation of brain tissue regions. We present a robust, generalisable defacing pipeline for high-resolution MRI that integrates atlas-based registration with brain masking. Our method was evaluated on 2,566 heterogeneous clinical scans for meningioma and achieved a 99.92 per cent success rate (2,564/2,566) upon visual inspection. Excellent anatomical preservation is demonstrated with a Dice similarity coefficient of 0.9975 plus or minus 0.0023 between brain masks automatically extracted from the original and defaced volumes. Source code is available at https://github.com/cai4cai/defacing_pipeline. |
| title | A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.12999 |