An Efficient Tongue Segmentation Method Using Synthetic Data Based on Fourier Transform and FASSP

Tongue segmentation is a key step to realize the intelligent tongue diagnosis of traditional Chinese medicine. The segmentation based on deep neural networks has achieved good results with a large amount of data with pixel-level annotation. But it is difficult to obtain a large amount of pixel-level labeled real data. In this work, we propose a simple and effective tongue segmentation method using synthetic data with rich pixel-level annotations. Firstly, we adopt the Fourier transform and its inverse transform to reduce the distribution difference between the target and source domains. Then we construct fold atrous spatial pyramid pooling (FASPP) to get multi-scale information and enhance the correlation among local features for tongue data with different sizes. Finally, the experimental results show the effectiveness of our approach.