QA-TSN: QuickAccurate Tongue Segmentation Net

Abstract

Tongue segmentation is an essential part for computer-aided tongue diagnosis. Since of similar color and texture between tongue body and non-tongue body, such as lips and face, existing methods produce the lack of accuracy and completeness for tongue segmentation results. Moreover, small samples in tongue datasets lead under-fitting on CNN-based methods which always produce poor segmentation. To solve these problems, we designed the quick accurate tongue segmentation net (QA-TSN) to segment tongue body. To alleviate small sample problem, in the proposed method, a tongue-style transfer generation net(T-STGN) was propose to synthesize tongue images. In T-STGN, a novel encoder–decoder structure with two encoder with a global rendering block was used to refine global characteristics of synthetic tongue images. For real-time tongue segmentation, quicker tongue segmentation net (QTSN) was proposed in QA-TSN. In QTSN, we used an encoder–decoder structure with modified partial convolution (MPConv) to expedite the computation for real-time segmentation. To smooth the segments of tongue body, a novel loss function of tongue segmentation loss (TSL) was proposed. In TSL, tongue edge loss (TEL) was used to smooth the boundary of segmentation of tongue body and tongue area loss (TAL) was proposed to improve the fragmentation of segmentation results. Experiments conducted on tongue datasets achieved an IoU of 98.0307 and a Dice score of 99.0738, with a frame rate of 75.35, outperforming all other methods involved in the experiment. These results demonstrate the effectiveness of the proposed QA-TSN.