LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography
Coronary artery stenosis detection by invasive coronary angiography plays a pivotal role in computer-aided diagnosis and treatment. However, it faces the challenge of stenotic morphology confusion stemming from coronary-background similarity, varied morphology, and small-area stenoses. Furthermore,...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1558495/full |
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| author | Jiaxin Li Xiang Tang Xuesong Wang |
| author_facet | Jiaxin Li Xiang Tang Xuesong Wang |
| author_sort | Jiaxin Li |
| collection | DOAJ |
| description | Coronary artery stenosis detection by invasive coronary angiography plays a pivotal role in computer-aided diagnosis and treatment. However, it faces the challenge of stenotic morphology confusion stemming from coronary-background similarity, varied morphology, and small-area stenoses. Furthermore, existing automated methods ignore long-temporal information mining. To address these limitations, this paper proposes a long-term temporal enhanced You Only Look Once (YOLO) method for automatic stenosis detection and assessment in invasive coronary angiography. Our approach integrates long-term temporal information and spatial information for stenosis detection with state-space models and YOLOv8. First, a spatial-aware backbone based on a dynamic Transformer and C2f Convolution of YOLOv8 combines the local and global feature extraction to distinguish the coronary regions from the background. Second, a spatial–temporal multi-level fusion neck integrates the long-term temporal and spatial features to handle varied stenotic morphology. Third, a detail-aware detection head leverages low-level information for accurate identification of small stenoses. Extensive experiments on 350 invasive coronary angiography (ICA) video sequences demonstrate the model’s superior performance over seven state-of-the-art methods, particularly in detecting small stenoses (<50%), which were previously underexplored. |
| format | Article |
| id | doaj-art-ca8b0104addc4bbc8b0230b13ae32f65 |
| institution | OA Journals |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-ca8b0104addc4bbc8b0230b13ae32f652025-08-20T01:55:16ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-04-011210.3389/fmolb.2025.15584951558495LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiographyJiaxin Li0Xiang Tang1Xuesong Wang2School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, ChinaSchool of Biomedical Engineering, Sun Yat-sen University, Shenzhen, ChinaCollege of Mining Engineering, University of Science and Technology Liaoning, Anshan, ChinaCoronary artery stenosis detection by invasive coronary angiography plays a pivotal role in computer-aided diagnosis and treatment. However, it faces the challenge of stenotic morphology confusion stemming from coronary-background similarity, varied morphology, and small-area stenoses. Furthermore, existing automated methods ignore long-temporal information mining. To address these limitations, this paper proposes a long-term temporal enhanced You Only Look Once (YOLO) method for automatic stenosis detection and assessment in invasive coronary angiography. Our approach integrates long-term temporal information and spatial information for stenosis detection with state-space models and YOLOv8. First, a spatial-aware backbone based on a dynamic Transformer and C2f Convolution of YOLOv8 combines the local and global feature extraction to distinguish the coronary regions from the background. Second, a spatial–temporal multi-level fusion neck integrates the long-term temporal and spatial features to handle varied stenotic morphology. Third, a detail-aware detection head leverages low-level information for accurate identification of small stenoses. Extensive experiments on 350 invasive coronary angiography (ICA) video sequences demonstrate the model’s superior performance over seven state-of-the-art methods, particularly in detecting small stenoses (<50%), which were previously underexplored.https://www.frontiersin.org/articles/10.3389/fmolb.2025.1558495/fullcoronary artery diseasestenosis detectionstate-space modelMambaYOLO |
| spellingShingle | Jiaxin Li Xiang Tang Xuesong Wang LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography Frontiers in Molecular Biosciences coronary artery disease stenosis detection state-space model Mamba YOLO |
| title | LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography |
| title_full | LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography |
| title_fullStr | LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography |
| title_full_unstemmed | LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography |
| title_short | LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography |
| title_sort | lt yolo long term temporal enhanced yolo for stenosis detection on invasive coronary angiography |
| topic | coronary artery disease stenosis detection state-space model Mamba YOLO |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1558495/full |
| work_keys_str_mv | AT jiaxinli ltyololongtermtemporalenhancedyoloforstenosisdetectiononinvasivecoronaryangiography AT xiangtang ltyololongtermtemporalenhancedyoloforstenosisdetectiononinvasivecoronaryangiography AT xuesongwang ltyololongtermtemporalenhancedyoloforstenosisdetectiononinvasivecoronaryangiography |