Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis

Léo Lebrat, Rodrigo Santa Cruz, Remi Chierchia, Yulia Arzhaeva, Mohammad Ali Armin +10 more
11/27/2023
cs.CV

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Abstract

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.

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Cite this paper

@article{lebrat2023syndwound,
  title  = {Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis},
  author = {Léo Lebrat and Rodrigo Santa Cruz and Remi Chierchia and Yulia Arzhaeva and Mohammad Ali Armin and Joshua Goldsmith and Jeremy Oorloff and Prithvi Reddy and Chuong Nguyen and Lars Petersson and Michelle Barakat-Johnson and Georgina Luscombe and Clinton Fookes and Olivier Salvado and David Ahmedt-Aristizabal},
  year   = {2023},
  eprint = {2311.15836},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.15836v2}
}

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