Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis
This addresses a data bottleneck for researchers and clinicians in wound management, though it is incremental as it builds on existing techniques.
The paper tackles the shortage of diverse training datasets for wound analysis by introducing Syn3DWound, a synthetic dataset with 2D and 3D annotations, and proposes baseline methods and a benchmarking framework for automated 3D morphometry and segmentation.
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.