Automatic Segmentation of Color Tongue Image Using Deep Asymmetric Convolution Skip Net

Segmentation of the tongue body from color images is vital for tongue diagnoses in traditional Chinese medicine. In tongue images, the tongue body is easily confused with the skin and lips, and the shadow also causes incorrect segmentation. To address these issues, we proposed a novel ACS-Net for tongue image segmentation and implemented the end-to-end form. In our ACS-Net architecture, the following innovations proposed: (1) ordinary convolution was replaced with ACB Module, (2) decoder block restores the features extracted by the encoder block, (3) skip connections are implemented between and within blocks. We use our own datasets named S1 and S2 that collected from the partner hospital. The collection methods of these two datasets are different: S1 was collected by professionals while S2 was taken by nurses. The method achieved state-of-the-art results on both two datasets, we use two metrics to reflect the segmentation performance, which are accuracy (acc) and mean intersection over Union (mIoU), in which the acc reaches 0.984 on S1 and 0.981 on S2; the mIoU reaches 0.925 on S1 and 0.958 on S2.