Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography
Coronary heart disease (CHD), a leading cause of global mortality, requires precise and early diagnosis for effective intervention. Coronary computed tomography angiography (CCTA) has emerged as a non-invasive modality for detailed coronary artery visualization; however, automatic and accurate segme...
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2025-01-01
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| author | Bo Zhao Jianjun Peng Ce Chen Yongyan Fan Kai Zhang Yang Zhang |
| author_facet | Bo Zhao Jianjun Peng Ce Chen Yongyan Fan Kai Zhang Yang Zhang |
| author_sort | Bo Zhao |
| collection | DOAJ |
| description | Coronary heart disease (CHD), a leading cause of global mortality, requires precise and early diagnosis for effective intervention. Coronary computed tomography angiography (CCTA) has emerged as a non-invasive modality for detailed coronary artery visualization; however, automatic and accurate segmentation of coronary structures from CCTA images remains challenging. Conventional convolutional neural networks (CNNs), despite their success in medical imaging, face limitations in capturing the complex, long-range dependencies in coronary artery images due to their localized receptive fields. Vision transformers, with their self-attention mechanisms, offer a global perspective, yet demand extensive data and computational resources, making them less adaptable for the often limited medical imaging datasets. This research addresses these challenges by proposing TransCHD, a hybrid CNN-Transformer architecture developed for coronary artery segmentation in CCTA. TransCHD incorporates a Contextual Representation Learning (CRL) module and a Spatially-Aware Feature (SAF) module, enabling both local feature extraction and global contextual awareness within a unified architecture. The CRL module mitigates spatial continuity disruptions caused by standard patch-based transformers, while the SAF module enhances spatial locality and preserves fine-grained anatomical details essential for accurate segmentation. The segmentation outcomes are clinically significant as they provide quantitative assessments of arterial stenosis, plaque characterization, and ischemia-prone regions, supporting risk assessment and treatment planning. Trained and evaluated on the CorArtTS2020 dataset, TransCHD achieved superior performance compared to state-of-the-art CNN- and transformer-based models, with a Dice score of 0.81 and an Intersection over Union (IoU) of 0.65. Results show that our proposed TransCHD is effective in CCTA segmentation. |
| format | Article |
| id | doaj-art-6414b9ce872640ce93800b1fdf8b001f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-6414b9ce872640ce93800b1fdf8b001f2025-01-21T00:02:22ZengIEEEIEEE Access2169-35362025-01-0113101771019310.1109/ACCESS.2025.352863810838557Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography AngiographyBo Zhao0Jianjun Peng1https://orcid.org/0009-0008-3094-165XCe Chen2Yongyan Fan3Kai Zhang4Yang Zhang5Department of Cardiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaCoronary heart disease (CHD), a leading cause of global mortality, requires precise and early diagnosis for effective intervention. Coronary computed tomography angiography (CCTA) has emerged as a non-invasive modality for detailed coronary artery visualization; however, automatic and accurate segmentation of coronary structures from CCTA images remains challenging. Conventional convolutional neural networks (CNNs), despite their success in medical imaging, face limitations in capturing the complex, long-range dependencies in coronary artery images due to their localized receptive fields. Vision transformers, with their self-attention mechanisms, offer a global perspective, yet demand extensive data and computational resources, making them less adaptable for the often limited medical imaging datasets. This research addresses these challenges by proposing TransCHD, a hybrid CNN-Transformer architecture developed for coronary artery segmentation in CCTA. TransCHD incorporates a Contextual Representation Learning (CRL) module and a Spatially-Aware Feature (SAF) module, enabling both local feature extraction and global contextual awareness within a unified architecture. The CRL module mitigates spatial continuity disruptions caused by standard patch-based transformers, while the SAF module enhances spatial locality and preserves fine-grained anatomical details essential for accurate segmentation. The segmentation outcomes are clinically significant as they provide quantitative assessments of arterial stenosis, plaque characterization, and ischemia-prone regions, supporting risk assessment and treatment planning. Trained and evaluated on the CorArtTS2020 dataset, TransCHD achieved superior performance compared to state-of-the-art CNN- and transformer-based models, with a Dice score of 0.81 and an Intersection over Union (IoU) of 0.65. Results show that our proposed TransCHD is effective in CCTA segmentation.https://ieeexplore.ieee.org/document/10838557/Convolutional neural networksvision transformercardiovascular segmentationdeep learning |
| spellingShingle | Bo Zhao Jianjun Peng Ce Chen Yongyan Fan Kai Zhang Yang Zhang Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography IEEE Access Convolutional neural networks vision transformer cardiovascular segmentation deep learning |
| title | Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography |
| title_full | Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography |
| title_fullStr | Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography |
| title_full_unstemmed | Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography |
| title_short | Diagnosis of Coronary Heart Disease Through Deep Learning-Based Segmentation and Localization in Computed Tomography Angiography |
| title_sort | diagnosis of coronary heart disease through deep learning based segmentation and localization in computed tomography angiography |
| topic | Convolutional neural networks vision transformer cardiovascular segmentation deep learning |
| url | https://ieeexplore.ieee.org/document/10838557/ |
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