MPCCN: A Symmetry-Based Multi-Scale Position-Aware Cyclic Convolutional Network for Retinal Vessel Segmentation

In medical image analysis, precise retinal vessel segmentation is crucial for diagnosing and managing ocular diseases as the retinal vascular network reflects numerous health indicators. Despite decades of development, challenges such as intricate textures, vascular ruptures, and undetected areas persist, particularly in accurately segmenting small vessels and addressing low contrast in imaging. This study introduces a novel segmentation approach called MPCCN that combines position-aware cyclic convolution (PCC) with multi-scale resolution input to tackle these challenges. By integrating standard convolution with PCC, MPCCN effectively captures both global and local features. A multi-scale input module enhances feature extraction, while a weighted-shared residual and guided attention module minimizes background noise and emphasizes vascular structures. Our approach achieves sensitivity values of 98. 87%, 99. 17%, and 98. 88%; specificity values of 98. 93%, 97. 25%, and 99. 20%; accuracy scores of 97. 38%, 97. 85%, and 97. 75%; and AUC values of 98. 90%, 99. 15%, and 99. 05% on the DRIVE, STARE, and CHASEDB1 datasets, respectively. In addition, it records F1 scores of 90. 93%, 91. 00%, and 90. 55%. Experimental results demonstrate that our method outperforms existing techniques, especially in detecting small vessels.