Abstract
To address the challenge of medical image segmentation caused by blurred edges, some researchers have leveraged edge information to improve segmentation performance. However, the current mainstream edge enhanced medical image segmentation networks are limited by the unidirectional information flow mechanism of the encoder decoder architecture, which may affect the network's inference accuracy for complex anatomical structures. In this paper, we propose a novel edge guided bidirectional iterative network in medical image segmentation (EGBINet), which adopts a cyclic architecture to enable bidirectional flow of edge information and region information between the encoder and decoder, thereby enhancing segmentation performance. Specifically, complementary information is generated by fusing edge features with multi-level region features, constructing an enhanced feedforward information pathway from the encoder to the decoder. Within the feedback mechanism from the decoder to the encoder, region feature representations and edge feature representations are reciprocally propagated, enabling iterative optimization of hierarchical feature representations. This bidirectional flow allows the encoder to dynamically respond to the decoder's requirements. Furthermore, to improve the aggregation quality of local edge information and multi-level global regional information, we introduce a transformer-based multi-level adaptive collaboration module (TACM). TACM groups local information and multi-level global information, adaptively adjusts their weights according to the aggregation quality, significantly improving the feature fusion quality. Experimental results on multiple medical image segmentation datasets demonstrate that our proposed EGBINet achieves remarkable performance advantages compared to state-of-the-art methods, particularly in edge preservation and complex structure segmentation accuracy, validating the superiority of our proposed network architecture.