  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,
when confronted with tongue images that differ from the training set or possess
challenging backgrounds, these methods demonstrate limited performance. 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 leverages its
learned prior knowledge from natural images, enabling the achievement of
zero-shot segmentation for various types of tongue images. 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. Even when dealing with
challenging background tongue images, TongueSAM achieves a mIoU of 95.23\%
under zero-shot conditions, surpassing other segmentation methods. As far as we
know, this is the first application of large-scale pretrained model for tongue
segmentation. The project mentioned in this paper is currently publicly
available.
