MEAT-SAM: More Efficient Automated Tongue Segmentation Model

Abstract

In Traditional Chinese Medicine (TCM) diagnostics, the appearance of the tongue is a crucial indicator of health. TCM practitioners traditionally assess the tongue’s shape, color, texture, and other features to aid diagnosis. With advancements in technology, digitizing and analyzing tongue images using computer has become possible, making tongue image segmentation an important step in realizing automated tongue diagnosis. While existing network models have shown good results, they often struggle with tongue images against complex backgrounds, particularly on resource-limited edge devices. To tackle this challenge, this paper introduces a novel, more efficient automated tongue image segmentation model (MEAT-SAM). MEAT-SAM leverages a lightweight large model, a first in the field of tongue image segmentation. Compared to previous models, MEAT-SAM reduces the overall model parameters, improves segmentation speed, and enables operation on more resource-constrained edge devices. Despite its efficiency, MEAT-SAM maintains performance close to the state-of-the-art (SOTA) even with complex tongue image backgrounds. Tested on three different datasets (TongueDataset01, TongueDataset02, TongueDataset03), MEAT-SAM achieved IoU of 96.63%, 95.58%, and 95.07%, demonstrating excellent generalization and robustness against various complex background conditions in tongue images. Furthermore, MEAT-SAM can run effectively on the computationally limited Jetson Nano single-board computer, achieving similar segmentation effects as in experimental testing.

Keywords

• Automated
• efficient
• lightweight
• image segmentation
• robustness