As one of the key methods of Traditional Chinese Medicine inspection, tongue diagnosis manifests the advantages of simplicity and directness. Sublingual veins can provide essential information about human health. In order to automate tongue diagnosis, sublingual veins segmentation has become one important issue in the field of Chinese medicine medical image processing. At present, the primary methods for sublingual veins segmentation are traditional feature engineering methods and the feature representation methods represented by deep learning. The former, which mainly based on colour space, belongs to unsupervised classification method. The latter, which includes U-Net and other deep neural network models, belongs to supervised classification method. Current feature engineering methods can only capture low dimensional information, which makes it difficult to extract efficient features for sublingual veins. On the other hand, current deep learning methods use down-sampling structures, which manifest weak robustness and low accuracy. So, it is difficult for current segmentation approaches to recognize tiny branches of sublingual veins. To overcome the above limits, this paper proposes a novel two-stage semantic segmentation method for sublingual veins. In the first stage, a fully convolutional network without down-sampling is used to realize the accurate segmentation of the tongue that includes the sublingual veins to be segmented in the next stage. During the tongue segmentation, the proposed networks can effectively reduce the loss of medical images spatial feature information. At the same time, in order to expand the receptive field, the dilated convolution has been introduced to the proposed networks, which can capture multi-scale information of segmentation images. In the second stage, another fully convolutional network has been used to segment the sublingual veins on the base of the results from the first stage. In this model, proper dilated convolutional rates have been selected to avoid gridding issue. In order to keep the quality of the images to be segmented, several particular data pre-processing and post-processing have been used, which includes specular highlight removal, data augmentation, erosion and dilation. Finally, in order to evaluate the performance of the proposed model, segmentation results have been compared with the state-of-the-art methods on the base of the dataset from Shanghai University of Traditional Chinese Medicine. The effectiveness of sublingual veins segmentation has been proved.

舌诊作为中医检查的关键方法之一，体现了简单、直接的优点。舌下静脉可以提供有关人类健康的基本信息。为了实现舌诊断的自动化，舌下静脉分割已成为中医医学图像处理领域的一个重要问题。目前，舌下静脉分割的主要方法是传统的特征工程方法和以深度学习为代表的特征表示方法。前者主要基于色彩空间，属于无监督分类方法。后者包括 U-Net 和其他深度神经网络模型，属于监督分类方法。目前的特征工程方法只能捕获低维信息，这使得难以提取舌下静脉的有效特征。另一方面，当前的深度学习方法使用下采样结构，表现出鲁棒性弱和准确性低。因此，目前的分割方法很难识别舌下静脉的微小分支。为了克服上述限制，本文提出了一种新的舌下静脉两阶段语义分割方法。第一阶段采用无下采样的全卷积网络，实现对舌头的准确分割，包括下一阶段要分割的舌下静脉。在舌头分割过程中，所提出的网络可以有效减少医学图像空间特征信息的损失。同时，为了扩大感受野，在所提出的网络中引入了扩张卷积，可以捕获分割图像的多尺度信息。 在第二阶段，另一个全卷积网络被用来根据第一阶段的结果分割舌下静脉。在此模型中，选择了适当的膨胀卷积率以避免网格化问题。为了保持要分割的图像的质量，使用了几种特殊的数据预处理和后处理，其中包括镜面高光去除、数据增强、侵蚀和膨胀。最后，为了评估所提出的模型的性能，在上海中医药大学的数据集的基础上，将分割结果与最先进的方法进行了比较。舌下静脉分割的有效性已得到证明。