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Multi-OCDTNet: A Novel Multi-Scale Object Context Dilated Transformer Network for Retinal Blood Vessel Segmentation.
- Source :
-
International Journal of Pattern Recognition & Artificial Intelligence . Sep2023, Vol. 37 Issue 11, p1-36. 36p. - Publication Year :
- 2023
-
Abstract
- Image segmentation is an essential part of medical image processing, which plays a significant role in adjunctive therapy, disease diagnosis, and medical assessment. To solve the problem of insufficient extracting context information, especially for medical image segmentation, this paper proposes a novel network architecture of multi-scale object context dilated transformer network (Multi-OCDTNet) to improve the utilization and segmentation accuracy for context information. The multi-scale object context transformer module can extract the multi-scale context information of the image through a three-layer transformer structure in a parallel way. The dilated convolution self-aware module can enhance the awareness of multi-scale context information in the feature map through layering transformer block groups and a set of transformer layers. In addition, we propose a composite weight-assigned-based loss function based on DDCLoss and Focal Tversky Loss to improve the stability of the segmentation performance of Multi-OCDTNet by adjusting the weight. The performance of Multi-OCDTNet is validated on the DRIVE and STARE datasets with segmentation accuracy of 97.17% and 97.84%, respectively, indicating the Multi-OCDTNet network possesses a significant competitive advantage in improving the segmentation performance of retinal vessel images. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 37
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 172435025
- Full Text :
- https://doi.org/10.1142/S0218001423570094