Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation

TL;DR

This paper introduces BGHNet, a lightweight end-to-end network with hierarchical modules and hybrid loss for accurate, real-time tongue segmentation despite complex surface details and shape variability.

Contribution

The paper proposes a novel Boundary Guidance Hierarchical Network with hierarchical modules and hybrid loss for improved tongue segmentation accuracy.

Findings

Achieves state-of-the-art tongue segmentation performance.

Contains only 15.45 million parameters and runs with 11.22 GFLOPS.

Effectively handles complex tongue surface details and shape variations.

Abstract

Automated tongue image segmentation in tongue images is a challenging task for two reasons: 1) there are many pathological details on the tongue surface, which affect the extraction of the boundary; 2) the shapes of the tongues captured from various persons (with different diseases) are quite different. To deal with the challenge, a novel end-to-end Boundary Guidance Hierarchical Network (BGHNet) with a new hybrid loss is proposed in this paper. In the new approach, firstly Context Feature Encoder Module (CFEM) is built upon the bottomup pathway to confront with the shrinkage of the receptive field. Secondly, a novel hierarchical recurrent feature fusion module (HRFFM) is adopt to progressively and hierarchically refine object maps to recover image details by integrating local context information. Finally, the proposed hybrid loss in a four hierarchy-pixel, patch, map and boundary guides the network to effectively segment the tongue regions and accurate tongue boundaries. BGHNet is applied to a set of tongue images. The experimental results suggest that the proposed approach can achieve the latest tongue segmentation performance. And in the meantime, the lightweight network contains only 15.45M parameters and performs only 11.22GFLOPS.