Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916145585455104 |
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| author | Lebrat, Léo Cruz, Rodrigo Santa Chierchia, Remi Arzhaeva, Yulia Armin, Mohammad Ali Goldsmith, Joshua Oorloff, Jeremy Reddy, Prithvi Nguyen, Chuong Petersson, Lars Barakat-Johnson, Michelle Luscombe, Georgina Fookes, Clinton Salvado, Olivier Ahmedt-Aristizabal, David |
| author_facet | Lebrat, Léo Cruz, Rodrigo Santa Chierchia, Remi Arzhaeva, Yulia Armin, Mohammad Ali Goldsmith, Joshua Oorloff, Jeremy Reddy, Prithvi Nguyen, Chuong Petersson, Lars Barakat-Johnson, Michelle Luscombe, Georgina Fookes, Clinton Salvado, Olivier Ahmedt-Aristizabal, David |
| contents | Wound management poses a significant challenge, particularly for bedridden patients and the elderly. Accurate diagnostic and healing monitoring can significantly benefit from modern image analysis, providing accurate and precise measurements of wounds. Despite several existing techniques, the shortage of expansive and diverse training datasets remains a significant obstacle to constructing machine learning-based frameworks. This paper introduces Syn3DWound, an open-source dataset of high-fidelity simulated wounds with 2D and 3D annotations. We propose baseline methods and a benchmarking framework for automated 3D morphometry analysis and 2D/3D wound segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_15836 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis Lebrat, Léo Cruz, Rodrigo Santa Chierchia, Remi Arzhaeva, Yulia Armin, Mohammad Ali Goldsmith, Joshua Oorloff, Jeremy Reddy, Prithvi Nguyen, Chuong Petersson, Lars Barakat-Johnson, Michelle Luscombe, Georgina Fookes, Clinton Salvado, Olivier Ahmedt-Aristizabal, David Computer Vision and Pattern Recognition Wound management poses a significant challenge, particularly for bedridden patients and the elderly. Accurate diagnostic and healing monitoring can significantly benefit from modern image analysis, providing accurate and precise measurements of wounds. Despite several existing techniques, the shortage of expansive and diverse training datasets remains a significant obstacle to constructing machine learning-based frameworks. This paper introduces Syn3DWound, an open-source dataset of high-fidelity simulated wounds with 2D and 3D annotations. We propose baseline methods and a benchmarking framework for automated 3D morphometry analysis and 2D/3D wound segmentation. |
| title | Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.15836 |