基于边界引导的层次网络实时舌体分割
Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation
摘要：
舌图像的自动分割是一项具有挑战性的任务，原因有两个：1）舌表面有许多病理细节，这影响了边界的提取; 2）从不同的人（患有不同疾病）捕获的舌的形状是相当不同的。为了应对这一挑战，本文提出了一种新的端到端边界制导分层网络（BGHNet）与一个新的混合损失。在新方法中，首先，上下文特征编码器模块（CFEM）是建立在自底向上的路径，以面对的感受野萎缩。其次，采用一种新的分层递归特征融合模块（HRFFM），通过融合局部上下文信息，逐步和分层地细化对象映射，以恢复图像细节。最后，提出的混合损失在四个层次-像素，补丁，地图和边界指导网络有效地分割舌头区域和准确的舌头边界。将BGHNet应用于一组舌图像。实验结果表明，该方法可以实现最新的舌分割性能。同时，轻量级网络仅包含15.45M参数，仅执行11.22GFLOPS。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
