目的 为实现中医面像部分区域的精准分割，提出一种融合Seg-UNet的中医面像分割网络模型。方法 采用Seg-Net网络中的最大池化索引将U-Net网络中的上采样改为上池化来改进U-Net网络。在U-Net网络原编码阶段的池化过程通过池化索引保留权重信息，上采样过程即可利用该索引实现特征图矩阵的扩充。在此基础上增加一层卷积扩增通道数，改进原网络中将特征图矩阵直接复制的上采样方式，从而降低池化过程中权重信息的损失。将Seg-UNet网络模型分别对脸颊、额头和嘴唇3个部位进行分割训练和测试。结果 对中医面像部分区域分割精度高，分割效果优于传统U-Net和Seg-Net网络模型，采用准确率（Acc）、Dice系数、平均交并比（MIoU）作为评价指标。结论 本研究结合深度学习方法实现了较好的中医面像部分区域分割效果。

Objective In order to achieve accurate segmentation of part regions of TCM (Chinese Traditional Medicine) facial image, a TCM facial image segmentation network model integrating Seg-UNet was proposed.Methods The maximum pooling index in Seg-Net network is used to change up-sampling into up-pooling to improve U-Net network. In the process of pooling in the original coding stage of U-Net network, the weight information is retained by pooling index, and the feature graph matrix can be extended by using this index in the up-sampling process. On this basis, a layer of convolutional amplification channel number is added to improve the up-sampling method of directly copying the feature graph matrix in the original network, so as to reduce the loss of weight information in the process of pooling. Seg-UNet network model was used for segmentation training and testing of cheek, forehead and lip.Results The segmentation effect is better than traditional U-Net and Seg-Net network algorithms, and the accuracy rate (Acc), Dice coefficient and Mean Intersection over Union (MIoU) are used as evaluation indexes.Conclusion Combined with the deep learning method, this study achieves a good effect of partial region segmentation of traditional Chinese medicine face image.

国家科学技术部重点研发计划中医药现代化研究专项（2018YFC1704400）：阴虚证辩证标准的系统研究，负责人：周作建；2021年度江苏省高校哲学社会科学研究一般项目（2021SJA0319）：智慧中医的发展趋势及面临问题研究，负责人：李红岩。