3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915360419086336 |
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| author | Qin, Xiaoyang Huang, Hao Lin, Shuaichen Zeng, Xinhao Cao, Kaizhi Wu, Renxiong Huang, Yuming Yang, Junqing Liu, Yong Li, Gang Ni, Guangming |
| author_facet | Qin, Xiaoyang Huang, Hao Lin, Shuaichen Zeng, Xinhao Cao, Kaizhi Wu, Renxiong Huang, Yuming Yang, Junqing Liu, Yong Li, Gang Ni, Guangming |
| contents | Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition holds pivotal importance in averting and identifying PCI complications that lead to in-stent restenosis. Here we proposed a novel three-dimensional (3D) distance-color-coded assessment (DccA)for PCI stent apposition via deep-learning-based 3D multi-object segmentation in intravascular optical coherence tomography (IV-OCT). Our proposed 3D DccA accurately segments 3D vessel lumens and stents in IV-OCT images, using a spatial matching network and dual-layer training with style transfer. It quantifies and maps stent-lumen distances into a 3D color space, facilitating 3D visual assessment of PCI stent apposition. Achieving over 95% segmentation precision, our proposed DccA enhances clinical evaluation of PCI stent deployment and supports personalized treatment planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20055 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation Qin, Xiaoyang Huang, Hao Lin, Shuaichen Zeng, Xinhao Cao, Kaizhi Wu, Renxiong Huang, Yuming Yang, Junqing Liu, Yong Li, Gang Ni, Guangming Computer Vision and Pattern Recognition Optics Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition holds pivotal importance in averting and identifying PCI complications that lead to in-stent restenosis. Here we proposed a novel three-dimensional (3D) distance-color-coded assessment (DccA)for PCI stent apposition via deep-learning-based 3D multi-object segmentation in intravascular optical coherence tomography (IV-OCT). Our proposed 3D DccA accurately segments 3D vessel lumens and stents in IV-OCT images, using a spatial matching network and dual-layer training with style transfer. It quantifies and maps stent-lumen distances into a 3D color space, facilitating 3D visual assessment of PCI stent apposition. Achieving over 95% segmentation precision, our proposed DccA enhances clinical evaluation of PCI stent deployment and supports personalized treatment planning. |
| title | 3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation |
| topic | Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2410.20055 |