  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.
