Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detect...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Journal of Cardiovascular Development and Disease |
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| Online Access: | https://www.mdpi.com/2308-3425/9/10/326 |
| _version_ | 1797407119643770880 |
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| author | Pankaj K. Jain Abhishek Dubey Luca Saba Narender N. Khanna John R. Laird Andrew Nicolaides Mostafa M. Fouda Jasjit S. Suri Neeraj Sharma |
| author_facet | Pankaj K. Jain Abhishek Dubey Luca Saba Narender N. Khanna John R. Laird Andrew Nicolaides Mostafa M. Fouda Jasjit S. Suri Neeraj Sharma |
| author_sort | Pankaj K. Jain |
| collection | DOAJ |
| description | Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment. |
| first_indexed | 2024-03-09T03:36:40Z |
| format | Article |
| id | doaj.art-9bab9e1298594aa1a6b21e850552a347 |
| institution | Directory Open Access Journal |
| issn | 2308-3425 |
| language | English |
| last_indexed | 2024-03-09T03:36:40Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Cardiovascular Development and Disease |
| spelling | doaj.art-9bab9e1298594aa1a6b21e850552a3472023-12-03T14:48:03ZengMDPI AGJournal of Cardiovascular Development and Disease2308-34252022-09-0191032610.3390/jcdd9100326Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence ParadigmPankaj K. Jain0Abhishek Dubey1Luca Saba2Narender N. Khanna3John R. Laird4Andrew Nicolaides5Mostafa M. Fouda6Jasjit S. Suri7Neeraj Sharma8School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, IndiaSchool of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, IndiaDepartment of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, ItalyDepartment of Cardiology, Indraprastha APOLLO Hospital, New Delhi 110076, IndiaHeart and Vascular Institute, Adventist Heath St. Helena, St. Helena, CA 94574, USAVascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2409, CyprusDepartment of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USAStroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USADepartment of Electronics and Communication, Shree Mata Vaishno Devi University, Jammu 182301, IndiaStroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.https://www.mdpi.com/2308-3425/9/10/326atherosclerosisstrokeCVDICACCAplaque segmentation |
| spellingShingle | Pankaj K. Jain Abhishek Dubey Luca Saba Narender N. Khanna John R. Laird Andrew Nicolaides Mostafa M. Fouda Jasjit S. Suri Neeraj Sharma Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm Journal of Cardiovascular Development and Disease atherosclerosis stroke CVD ICA CCA plaque segmentation |
| title | Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm |
| title_full | Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm |
| title_fullStr | Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm |
| title_full_unstemmed | Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm |
| title_short | Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm |
| title_sort | attention based unet deep learning model for plaque segmentation in carotid ultrasound for stroke risk stratification an artificial intelligence paradigm |
| topic | atherosclerosis stroke CVD ICA CCA plaque segmentation |
| url | https://www.mdpi.com/2308-3425/9/10/326 |
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