Facial wound segmentation plays a crucial role in preoperative planning and
optimizing patient outcomes in various medical applications. In this paper, we
propose an efficient approach for automating 3D facial wound segmentation using
a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR
dataset and addresses the challenge of data imbalance through extensive
experimentation with different loss functions. To achieve accurate
segmentation, we conducted thorough experiments and selected a high-performing
model from the trained models. The selected model demonstrates exceptional
segmentation performance for complex 3D facial wounds. Furthermore, based on
the segmentation model, we propose an improved approach for extracting 3D
facial wound fillers and compare it to the results of the previous study. Our
method achieved a remarkable accuracy of 0.9999986\% on the test suite,
surpassing the performance of the previous method. From this result, we use 3D
printing technology to illustrate the shape of the wound filling. The outcomes
of this study have significant implications for physicians involved in
preoperative planning and intervention design. By automating facial wound
segmentation and improving the accuracy of wound-filling extraction, our
approach can assist in carefully assessing and optimizing interventions,
leading to enhanced patient outcomes. Additionally, it contributes to advancing
facial reconstruction techniques by utilizing machine learning and 3D
bioprinting for printing skin tissue implants. Our source code is available at
https://github.com/SIMOGroup/WoundFilling3D.

本研究提出了一种基于二流图卷积网络的自动 3D 面部创口分割方法，可应用于医学预操作规划及优化患者结果；该方法使用 Cir3D-FaIR 数据集对数据失衡进行了广泛的实验，并基于测试套件获得了出色的 3D 面部伤口填充精度，同时提出了改进方法，并采用 3D 打印技术，可协助诊断和优化手术，并发展使用机器学习和 3D 生物打印制造皮肤组织植入物。