CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion

Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term challenge. In this paper, based on an encoder–dec...

Full description

Saved in:
Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 23; p. 4743
Main Authors Gu, Yanan, Cao, Ruyi, Wang, Dong, Lu, Bibo
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2023
Subjects
Algorithms
Blood vessels
Connectivity
Datasets
Deep learning
Diagnostic imaging
Image contrast
Image processing
Modules
Morphology
Neural networks
Retina
China
Online AccessGet full text

Cover

More Information
Summary:Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term challenge. In this paper, based on an encoder–decoder framework, a novel retinal vessel segmentation model called CMP-UNet is proposed. Firstly, the Coarse and Fine Feature Aggregation module decouples and aggregates coarse and fine vessel features using two parallel branches, thus enhancing the model’s ability to extract features for vessels of various sizes. Then, the Multi-Scale Channel Adaptive Fusion module is embedded in the decoder to realize the efficient fusion of cascade features by mining the multi-scale context information from these features. Finally, to obtain more discriminative vascular features and enhance the connectivity of vascular structures, the Pyramid Feature Fusion module is proposed to effectively utilize the complementary information of multi-level features. To validate the effectiveness of the proposed model, it is evaluated on three publicly available retinal vessel segmentation datasets: CHASE_DB1, DRIVE, and STARE. The proposed model, CMP-UNet, reaches F1-scores of 82.84%, 82.55%, and 84.14% on these three datasets, with improvements of 0.76%, 0.31%, and 1.49%, respectively, compared with the baseline. The results show that the proposed model achieves higher segmentation accuracy and more robust generalization capability than state-of-the-art methods.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12234743