Dual Encoder DCNN for Efficient and Robust Segmentation of Retinal Vascular Tree From Fundus Images

ABSTRACT

Retinal vessel segmentation is a critical step for the diagnosis and monitoring of ocular and cardiovascular diseases. Despite advances in medical imaging, achieving accurate segmentation across varying fundus image resolutions remains challenging. Conventional convolutional neural networks (CNNs) often fail to capture both fine and large vascular structures due to repeated pooling operations and the limitations of static convolution kernels. To address these issues, we introduce in this work a robust and efficient CNN architecture specifically designed for retinal vessel segmentation. The key innovations of our approach consist of: (1) providing multi‐scale input strategy applied across all downsampling blocks to mitigate pooling drawbacks, (2) adopting a dual encoding mechanism with heterogeneous kernel processing to enrich feature extraction, and (3) fusing the extracted features that combine complementary encoder representations before decoding, thereby preserving spatial detail and enhancing discriminative features. The proposed method is evaluated on the DRIVE and HRF databases, which differ substantially in image resolution. The proposed network demonstrates strong performance, achieving a mean accuracy and sensitivity in the order of 97.62% and 97.22% on the DRIVE database, and in the order of 83.43% and 84.76% on the HRF database, respectively. Notably, the best fold attains sensitivity rates of 88.8% on DRIVE and 87.46% on HRF, highlighting the robustness of the method across both databases and their evaluation folds.