  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.
