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.

创伤管理对于卧床患者和老年人尤为具有挑战性。现代图像分析对准确的诊断和伤口愈合监测具有重要意义，能提供准确而精确的伤口测量结果。尽管存在多种技术，但广泛而多样的训练数据集的缺乏仍然是构建基于机器学习框架的重要障碍。本文介绍了 Syn3DWound，这是一个开放源代码的高保真模拟伤口数据集，具有 2D 和 3D 注释。我们提出了基线方法和用于自动化 3D 形态测量分析和 2D/3D 伤口分割的基准测试框架。