Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis
The carotid artery is a vital blood vessel that supplies oxygenated blood to the brain. Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis essential. While ultrasound (US) imaging is the primary diagnostic tool, evaluations by trained operators can be subjecti...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://dx.doi.org/10.1016/j.bspc.2024.106350 https://www.sciencedirect.com/science/article/pii/S1746809424004087 http://hdl.handle.net/10576/55190 |
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| _version_ | 1857415084529156096 |
|---|---|
| author | Najmath, Ottakath |
| author2 | Akbari, Younes Al-Maadeed, Somaya Ali Bouridane, Ahmed Zughaier, Susu M. Chowdhury, Muhammad E.H. |
| author2_role | author author author author author |
| author_facet | Najmath, Ottakath Akbari, Younes Al-Maadeed, Somaya Ali Bouridane, Ahmed Zughaier, Susu M. Chowdhury, Muhammad E.H. |
| author_role | author |
| dc.creator.none.fl_str_mv | Najmath, Ottakath Akbari, Younes Al-Maadeed, Somaya Ali Bouridane, Ahmed Zughaier, Susu M. Chowdhury, Muhammad E.H. |
| dc.date.none.fl_str_mv | 2024-05-20T08:26:51Z 2024-04-13 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.bspc.2024.106350 Ottakath, N., Akbari, Y., Al-Maadeed, S. A., Bouridane, A., Zughaier, S. M., & Chowdhury, M. E. (2024). Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis. Biomedical Signal Processing and Control, 94, 106350. 1746-8094 https://www.sciencedirect.com/science/article/pii/S1746809424004087 http://hdl.handle.net/10576/55190 94 1746-8108 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Carotid artery segmentation Transverse mode Attention network DoubleUnet Bottleneck attention module |
| dc.title.none.fl_str_mv | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The carotid artery is a vital blood vessel that supplies oxygenated blood to the brain. Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis essential. While ultrasound (US) imaging is the primary diagnostic tool, evaluations by trained operators can be subjective and imprecise. An automated approach can provide a more reliable and accurate evaluation of the carotid artery’s condition. To achieve this, the artery must first be identified and isolated from US images. This paper proposes an automated segmentation method for the carotid artery in transverse B-mode ultrasound images, using a Bi-attention DoubleUnet architecture which incorporates spatial attention and channel wise attention using Bottleneck attention module. The method is evaluated on a dataset of transverse images acquired from two devices. The obtained results exhibit higher performance than existing methods with a dice index of 95.96%, IoU of 97.92%, Precision of 98.35% and recall of 97.57% on combined dataset. Another variant of doubleUnet with SE layer and without ASPP (Artous Spatial Pyramidal Pooling) is presented where the modified DoubleUnet with SE realized an average IoU of 92.805%, Dice of 96.215%, precision of 98.82%, and recall of 93.84% representing average results across datasets. These approaches can significantly improve the efficiency and accuracy of carotid artery disease diagnosis and treatment, ultimately improving patient outcomes. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_45802462a205bd4f1fbb25a40b17e287 |
| identifier_str_mv | Ottakath, N., Akbari, Y., Al-Maadeed, S. A., Bouridane, A., Zughaier, S. M., & Chowdhury, M. E. (2024). Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis. Biomedical Signal Processing and Control, 94, 106350. 1746-8094 94 1746-8108 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/55190 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosisNajmath, OttakathAkbari, YounesAl-Maadeed, Somaya AliBouridane, AhmedZughaier, Susu M.Chowdhury, Muhammad E.H.Carotid artery segmentationTransverse modeAttention networkDoubleUnetBottleneck attention moduleThe carotid artery is a vital blood vessel that supplies oxygenated blood to the brain. Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis essential. While ultrasound (US) imaging is the primary diagnostic tool, evaluations by trained operators can be subjective and imprecise. An automated approach can provide a more reliable and accurate evaluation of the carotid artery’s condition. To achieve this, the artery must first be identified and isolated from US images. This paper proposes an automated segmentation method for the carotid artery in transverse B-mode ultrasound images, using a Bi-attention DoubleUnet architecture which incorporates spatial attention and channel wise attention using Bottleneck attention module. The method is evaluated on a dataset of transverse images acquired from two devices. The obtained results exhibit higher performance than existing methods with a dice index of 95.96%, IoU of 97.92%, Precision of 98.35% and recall of 97.57% on combined dataset. Another variant of doubleUnet with SE layer and without ASPP (Artous Spatial Pyramidal Pooling) is presented where the modified DoubleUnet with SE realized an average IoU of 92.805%, Dice of 96.215%, precision of 98.82%, and recall of 93.84% representing average results across datasets. These approaches can significantly improve the efficiency and accuracy of carotid artery disease diagnosis and treatment, ultimately improving patient outcomes.This document is the results of the research project funded by the Qatar University, High impact grant.This research work was made possible by research grant support (QUHI-CENG-22/23-548) from Qatar University Research Fund in Qatar.Elsevier2024-05-20T08:26:51Z2024-04-13Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.bspc.2024.106350Ottakath, N., Akbari, Y., Al-Maadeed, S. A., Bouridane, A., Zughaier, S. M., & Chowdhury, M. E. (2024). Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis. Biomedical Signal Processing and Control, 94, 106350.1746-8094https://www.sciencedirect.com/science/article/pii/S1746809424004087http://hdl.handle.net/10576/55190941746-8108enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/551902024-07-23T10:58:45Z |
| spellingShingle | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis Najmath, Ottakath Carotid artery segmentation Transverse mode Attention network DoubleUnet Bottleneck attention module |
| status_str | publishedVersion |
| title | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| title_full | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| title_fullStr | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| title_full_unstemmed | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| title_short | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| title_sort | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis |
| topic | Carotid artery segmentation Transverse mode Attention network DoubleUnet Bottleneck attention module |
| url | http://dx.doi.org/10.1016/j.bspc.2024.106350 https://www.sciencedirect.com/science/article/pii/S1746809424004087 http://hdl.handle.net/10576/55190 |