{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T10:41:29Z","timestamp":1778668889579,"version":"3.51.4"},"reference-count":52,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","funder":[{"name":"the Key Research and Development Program in Shaanxi Province","award":["2023-YBGY241"],"award-info":[{"award-number":["2023-YBGY241"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["Grant No.GK202105006"],"award-info":[{"award-number":["Grant No.GK202105006"]}]},{"name":"the Excellent Graduate Training Program of Shaanxi Normal University","award":["LHRCCX23207"],"award-info":[{"award-number":["LHRCCX23207"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,9,15]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218001423570094","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T16:18:05Z","timestamp":1690561085000},"source":"Crossref","is-referenced-by-count":3,"title":["Multi-OCDTNet: A Novel Multi-Scale Object Context Dilated Transformer Network for Retinal Blood Vessel Segmentation"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-8809","authenticated-orcid":false,"given":"Chengwei","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Teaching Technology, Ministry of Education, School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, P. R. 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