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 \url{https://github.com/SIMOGroup/WoundFilling3D}.

翻译：面部伤口分割在术前规划及优化患者预后中具有关键作用，广泛应用于各类医疗场景。本文提出一种基于双流图卷积网络的高效方法，实现三维面部伤口的自动分割。该方法利用Cir3D-FaIR数据集，通过系统性地实验不同损失函数，有效应对数据不平衡挑战。为达成精确分割，我们开展深入实验并从训练模型中筛选出高性能模型，该模型在复杂三维面部伤口分割任务中展现出卓越性能。进一步地，基于该分割模型，我们提出改进的三维面部伤口填充提取方法，并与既往研究结果进行对比。所提方法在测试集上实现了0.9999986%的优异精度，超越已有方法。基于此结果，我们采用三维打印技术呈现伤口填充形态。本研究对涉及术前规划与干预设计的临床医师具有重要启示意义。通过自动化面部伤口分割并提升伤口填充提取精度，本方法可辅助精细评估与优化干预方案，从而改善患者预后。此外，该方法结合机器学习与三维生物打印技术用于皮肤组织植入物制备，推动了面部重建技术的进步。本研究的源代码已公开于 \url{https://github.com/SIMOGroup/WoundFilling3D}。