Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network

• Published in: Applied sciences 2024-01, Vol.14 (1), p.465
• Main Authors: He, Xialan, Wang, Ting, Yang, Wankou
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; attention mechanisms; Blood vessels; Deep learning; Machine learning; Medical imaging equipment; multi-scale information; Neural networks; retinal vessel segmentation; selective kernel; Semantics
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14010465

Due to the limitations of traditional retinal blood vessel segmentation algorithms in feature extraction, vessel breakage often occurs at the end. To address this issue, a retinal vessel segmentation algorithm based on a modified U-shaped network is proposed in this paper. This algorithm can extract multi-scale vascular features and perform segmentation in an end-to-end manner. First, in order to improve the low contrast of the original image, pre-processing methods are employed. Second, a multi-scale residual convolution module is employed to extract image features of different granularities, while residual learning improves feature utilization efficiency and reduces information loss. In addition, a selective kernel unit is incorporated into the skip connections to obtain multi-scale features with varying receptive field sizes achieved through soft attention. Subsequently, to further extract vascular features and improve processing speed, a residual attention module is constructed at the decoder stage. Finally, a weighted joint loss function is implemented to address the imbalance between positive and negative samples. The experimental results on the DRIVE, STARE, and CHASE_DB1 datasets demonstrate that MU-Net exhibits better sensitivity and a higher Matthew’s correlation coefficient (0.8197, 0.8051; STARE: 0.8264, 0.7987; CHASE_DB1: 0.8313, 0.7960) compared to several state-of-the-art methods.