TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot

Tongue segmentation serves as the primary step in automated TCM tongue diagnosis, which plays a significant role in the diagnostic results. Currently, numerous deep learning based methods have achieved promising results. However, most of these methods exhibit mediocre performance on tongues different from the training set. To address this issue, this paper proposes a universal tongue segmentation model named TongueSAM based on SAM (Segment Anything Model). SAM is a large-scale pretrained interactive segmentation model known for its powerful zero-shot generalization capability. Applying SAM to tongue segmentation enables the segmentation of various types of tongue images with zero-shot. In this study, a Prompt Generator based on object detection is integrated into SAM to enable an end-to-end automated tongue segmentation method. Experiments demonstrate that TongueSAM achieves exceptional performance across various of tongue segmentation datasets, particularly under zero-shot. TongueSAM can be directly applied to other datasets without fine-tuning. As far as we know, this is the first application of large-scale pretrained model for tongue segmentation. The project and pretrained model of TongueSAM be publiced in :https://github.com/cshan-github/TongueSAM.