Wound3DAssist: A Practical Framework for 3D Wound Assessment
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909751432970240 |
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| author | Chierchia, Remi Cruz, Rodrigo Santa Lebrat, Léo Arzhaeva, Yulia Armin, Mohammad Ali Oorloff, Jeremy Nguyen, Chuong Salvado, Olivier Fookes, Clinton Ahmedt-Aristizabal, David |
| author_facet | Chierchia, Remi Cruz, Rodrigo Santa Lebrat, Léo Arzhaeva, Yulia Armin, Mohammad Ali Oorloff, Jeremy Nguyen, Chuong Salvado, Olivier Fookes, Clinton Ahmedt-Aristizabal, David |
| contents | Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks aided the measurement process, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates accurate 3D models from short handheld smartphone video recordings, enabling non-contact, automatic measurements that are view-independent and robust to camera motion. We integrate 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. We evaluate Wound3DAssist across digital models with known geometry, silicone phantoms, and real patients. Results show that the framework supports high-quality wound bed visualization, millimeter-level accuracy, and reliable tissue composition analysis. Full assessments are completed in under 20 minutes, demonstrating feasibility for real-world clinical use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wound3DAssist: A Practical Framework for 3D Wound Assessment Chierchia, Remi Cruz, Rodrigo Santa Lebrat, Léo Arzhaeva, Yulia Armin, Mohammad Ali Oorloff, Jeremy Nguyen, Chuong Salvado, Olivier Fookes, Clinton Ahmedt-Aristizabal, David Computer Vision and Pattern Recognition Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks aided the measurement process, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates accurate 3D models from short handheld smartphone video recordings, enabling non-contact, automatic measurements that are view-independent and robust to camera motion. We integrate 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. We evaluate Wound3DAssist across digital models with known geometry, silicone phantoms, and real patients. Results show that the framework supports high-quality wound bed visualization, millimeter-level accuracy, and reliable tissue composition analysis. Full assessments are completed in under 20 minutes, demonstrating feasibility for real-world clinical use. |
| title | Wound3DAssist: A Practical Framework for 3D Wound Assessment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.17635 |