D2PF-net: A dual-decoder progressive fusion network for retinal vessel segmentation

Zirun Zhang, Jingqin Lv, Yafeng Ren, Yuejin Zhang, Chengxin Ye, Nan Jiang, Donghong Ji, Qingzhong Zhou, Hongbin Zhang

Abstract: Accurate but efficient retinal vessel segmentation is crucial for clinical diagnosis. Although current deep learning-based methods have achieved satisfactory segmentation performance, there are still some challenges, such as the lack of microvessels and insufficient global modelling. To address these problems, we propose a Dual-Decoder Progressive Fusion Network (D2PF-Net). First, a multi-scale vessel enhancement (MVE) module is devised to alleviate the lack of microvessels. Second, a decoding feature fusion (DFF) module is embedded into the left branch of D2PF-Net to progressively enrich the decoder with sufficient global contextual information. Simultaneously, a cross-scale context fusion (CCF) module is inserted into the right branch of D2PF-Net to make the encoder focus more on multi-scale pathological information. Finally, a multi-scale feature aggregation (MFA) module is designed to aggregate complementary multi-scale semantics information from different branches. Extensive experiments have been conducted on five retinal vessel and two polyp image datasets, validating the efficiency and generalization ability of D2PF-Net, which is quite competitive compared to the state-of-the-art methods. More importantly, D2PF-Net is relatively lightweight, efficient and interpretable for real-time prediction, further promoting its practicality.

External IDs:dblp:journals/bspc/ZhangLRZYJJZZ26