Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis

Autor:	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
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: In the IEEE International Symposium on Biomedical Imaging (ISBI) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.15836 Zobrazit plný text záznamu View this record from Arxiv