| dc.contributor.author | Min Zhang | |
| dc.contributor.author | Huibin Wang | |
| dc.contributor.author | Liansheng Wang | |
| dc.contributor.author | Abdu Saif | |
| dc.contributor.author | Sobia Wassan | |
| dc.contributor.other | College of Computer and Information, Hohai University, Nanjing, 210000, Jiangsu, China; College of Electronic Information Engineering, Gannan University of Science and Technology, Ganzhou, 341000, Jiangxi, China | |
| dc.contributor.other | College of Computer and Information, Hohai University, Nanjing, 210000, Jiangsu, China; Corresponding author. | |
| dc.contributor.other | The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210000, Jiangsu, China | |
| dc.contributor.other | Department of Communication and Computer Engineering, Faculty of Engineering and IT, Taiz University, Yemen | |
| dc.contributor.other | School of Equipment Engineering, Jiangsu Urban and Rural, Changzhou, 213000, Jiangsu, China | |
| dc.date.accessioned | 2025-10-09T05:22:33Z | |
| dc.date.available | 2025-10-09T05:22:33Z | |
| dc.date.issued | 01-01-2024 | |
| dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S1110016823011262 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40978 | |
| dc.description.abstract | Accurate segmentation of X-ray angiography images is imperative for cardiovascular disease diagnosis. Despite significant strides in segmentation through deep learning, challenges persist in precisely delineating vessel edges. This paper introduces a Context Interactive Deep Network (CIDN) tailored for coronary vessel segmentation. CIDN integrates a Bio-inspired Attention Block (BAB) and a Multi-scale Interactive Block (MIB) to optimize the capture of spatial and edge information. The encoder-decoder facilitates optimal interaction between low-level features and high-level semantics. A compound loss function, amalgamating binary cross-entropy and active contour elasticity loss, enhances the recognition of intricate vessel edges. In experiments conducted on both public and private X-ray contrast images, the CIDN exhibits superior performance compared to the state-of-the-art Spatial Multi-scale Attention U-improved Network (SMAU-Net). Specifically, on dataset DCA1, the F1 value reached 0.7675, with an accuracy of 0.9795. Furthermore, on dataset JMA, the CIDN model achieved an F1 value of 0.8732, coupled with an accuracy of 0.9757. Accurate segmentation provides clinicians with precise vessel depictions, aiding in the identification of coronary stenosis and facilitating more informed diagnostic and therapeutic decisions. | |
| dc.language.iso | EN | |
| dc.publisher | Elsevier | |
| dc.subject.lcc | Engineering (General). Civil engineering (General) | |
| dc.title | CIDN: A context interactive deep network with edge-aware for X-ray angiography images segmentation | |
| dc.type | Article | |
| dc.description.keywords | X-ray angiography segmentation | |
| dc.description.keywords | Context-interactive | |
| dc.description.keywords | Bio-inspired attention | |
| dc.description.keywords | Multi-scale interactive | |
| dc.description.pages | 201-212 | |
| dc.description.doi | 10.1016/j.aej.2023.12.034 | |
| dc.title.journal | Alexandria Engineering Journal | |
| dc.identifier.oai | oai:doaj.org/journal:1d398666b02a4322a28ca9670c25207c | |
