Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis
<p>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...
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2024
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| _version_ | 1864513516023578624 |
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| author | Najmath Ottakath (17430912) |
| author2 | Younes Akbari (16303286) Somaya Ali Al-Maadeed (16864254) Ahmed Bouridane (2270131) Susu M. Zughaier (14151987) Muhammad E.H. Chowdhury (17151154) |
| author2_role | author author author author author |
| author_facet | Najmath Ottakath (17430912) Younes Akbari (16303286) Somaya Ali Al-Maadeed (16864254) Ahmed Bouridane (2270131) Susu M. Zughaier (14151987) Muhammad E.H. Chowdhury (17151154) |
| author_role | author |
| dc.creator.none.fl_str_mv | Najmath Ottakath (17430912) Younes Akbari (16303286) Somaya Ali Al-Maadeed (16864254) Ahmed Bouridane (2270131) Susu M. Zughaier (14151987) Muhammad E.H. Chowdhury (17151154) |
| dc.date.none.fl_str_mv | 2024-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.bspc.2024.106350 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Bi-attention_DoubleUNet_A_deep_learning_approach_for_carotid_artery_segmentation_in_transverse_view_images_for_non-invasive_stenosis_diagnosis/25826956 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering 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 | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Biomedical Signal Processing and Control<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.bspc.2024.106350" target="_blank">https://dx.doi.org/10.1016/j.bspc.2024.106350</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_767c8f53c7ea86ed4a6918ccfbdfae2a |
| identifier_str_mv | 10.1016/j.bspc.2024.106350 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25826956 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosisNajmath Ottakath (17430912)Younes Akbari (16303286)Somaya Ali Al-Maadeed (16864254)Ahmed Bouridane (2270131)Susu M. Zughaier (14151987)Muhammad E.H. Chowdhury (17151154)EngineeringBiomedical engineeringCarotid artery segmentationTransverse modeAttention networkDoubleUnetBottleneck attention module<p>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.</p><h2>Other Information</h2> <p> Published in: Biomedical Signal Processing and Control<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.bspc.2024.106350" target="_blank">https://dx.doi.org/10.1016/j.bspc.2024.106350</a></p>2024-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.bspc.2024.106350https://figshare.com/articles/journal_contribution/Bi-attention_DoubleUNet_A_deep_learning_approach_for_carotid_artery_segmentation_in_transverse_view_images_for_non-invasive_stenosis_diagnosis/25826956CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/258269562024-08-01T00:00:00Z |
| spellingShingle | Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis Najmath Ottakath (17430912) Engineering Biomedical engineering 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 | Engineering Biomedical engineering Carotid artery segmentation Transverse mode Attention network DoubleUnet Bottleneck attention module |