Enhanced pixel-wise style fusion network for stent malapposition recognition with re-parameterizing technique in OCT
文献类型：期刊论文
| 作者 | Zhang, Hua2; Zhang, Chen3,4; Li, Jing2,3; Xuan, Xuexi2; Wang, Mingjie2; Yi, Bo2; Xia, Kai2; Wang, Haiyan5; Yin, Lei2,3; Zhang, Xiaoqing1,2,3,6 |
| 刊名 | ARTIFICIAL INTELLIGENCE REVIEW
 |
| 出版日期 | 2025-12-12 |
| 卷号 | 59期号:2页码:26 |
| 关键词 | Coronary artery disease Stent malapposition recognition OCT Enhanced pixel-wise style fusion network Re-parameterizing |
| ISSN号 | 0269-2821 |
| DOI | 10.1007/s10462-025-11465-7 |
| 英文摘要 | Percutaneous coronary intervention with stent implantation has become a widely used strategy to treat coronary artery disease. Stent malapposition (SM) may increase the risk of late stent thrombosis due to stent tissue coverage reduction, attracting much attention clinically. Recently, optical coherence tomography (OCT) images have been utilized to visually assess the stent apposition/malapposition. However, automated OCT-based SM recognition has been under-explored previously. Therefore, this paper proposes a novel enhanced pixel-wise style fusion network (EPSF-Net) to recognize SM automatically from OCT images. In the EPSF-Net, considering SM information is subtle, we design a novel enhanced pixel-wise style fusion (EPSF) block, which first applies the pixel-wise style pooling to aggregate pixel-wise style context, then enhances pixel-wise style context with multi-scale learning, and finally fuses enhanced pixel-wise style context via a pixel-wise fusion operator. Moreover, the re-parameterizing technique is utilized to reduce the parameters and computational cost of EPSF at the inference stage. Additionally, considering there is no publicly available OCT dataset for SM recognition, we construct an OCT image dataset of SM, named SM-OCT, to validate the effectiveness of our method, which will be available. The extensive experiments on the SM-OCT dataset show that our proposed EPSF-Net achieves better SM recognition performance than state-of-the-art methods. Additionally, two publicly available OCT datasets are employed to verify the generalization of our method. |
| 资助项目 | National Natural Science Foundation of China[62302463] ; Henan Province Medical Science and Technology Joint Construction Project[LHGJ20220830] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001662361900001 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42901]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Li, Jing; Yin, Lei; Zhang, Xiaoqing |
| 作者单位 | 1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Intelligent Bioinformat, Shenzhen, Peoples R China 2.7th Peoples Hosp Zhengzhou, Dept Cardiol, Zhengzhou, Peoples R China 3.Henan Acad Innovat Med Sci, Inst Biol Therapy, Zhengzhou, Peoples R China 4.Guangxi Med Univ, Life Sci Inst, Nanning, Peoples R China 5.Zhengzhou Univ Aeronaut, Simulat Expt Ctr, Zhengzhou, Peoples R China 6.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Hua,Zhang, Chen,Li, Jing,et al. Enhanced pixel-wise style fusion network for stent malapposition recognition with re-parameterizing technique in OCT[J]. ARTIFICIAL INTELLIGENCE REVIEW,2025,59(2):26. |
| APA | Zhang, Hua.,Zhang, Chen.,Li, Jing.,Xuan, Xuexi.,Wang, Mingjie.,...&Zhang, Xiaoqing.(2025).Enhanced pixel-wise style fusion network for stent malapposition recognition with re-parameterizing technique in OCT.ARTIFICIAL INTELLIGENCE REVIEW,59(2),26. |
| MLA | Zhang, Hua,et al."Enhanced pixel-wise style fusion network for stent malapposition recognition with re-parameterizing technique in OCT".ARTIFICIAL INTELLIGENCE REVIEW 59.2(2025):26. |
入库方式： OAI收割
来源：计算技术研究所
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