TBE-Net: A Deep Network Based on Tree-like Branch Encoder for Medical Image Segmentation.

Abstract

In recent years, encoder-decoder-based network structures have been widely used in designing medical image segmentation models. However, these methods still face some limitations: 1) The network's feature extraction capability is limited, primarily due to insufficient attention to the encoder, resulting in a failure to extract rich and effective features. 2) Unidirectional stepwise decoding of smaller-sized feature maps restricts segmentation performance. To address the above limitations, we propose an innovative Tree-like Branch Encoder Network (TBE-Net), which adopts a tree-like branch encoder to better perform feature extraction and preserve feature information. Additionally, we introduce the Depth and Width Expansion (D-WE) module to expand the network depth and width at low parameter cost, thereby enhancing network performance. Furthermore, we design a Deep Aggregation Module (DAM) to better aggregate and process encoder features. Subsequently, we directly decode the aggregated features to generate the segmentation map. The experimental results show that, compared to other advanced algorithms, our method, with the lowest parameter cost, achieved improvements in the IoU metric on the TNBC, PH2, CHASE-DB1, STARE, and COVID-19-CT-Seg datasets by 1.6%, 0.46%, 0.81%, 1.96%, and 0.86%, respectively.