Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques
<p dir="ltr">Coronary artery disease (CAD) is a significant global health concern, emphasizing the need for reliable and automated diagnostic solutions. This study proposes a novel <u>deep learning </u>framework aimed at improving both full artery segmentation and <u&g...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513532034285568 |
|---|---|
| author | Abu Jaffor Morshedul Abedin (22803884) |
| author2 | Rusab Sarmun (17632269) Adam Mushtak (22048013) Mohamed Sultan Bin Mohamed Ali (22803887) Anwarul Hasan (1332066) Ponnuthurai Nagaratnam Suganthan (11274636) Muhammad E.H. Chowdhury (17151154) |
| author2_role | author author author author author author |
| author_facet | Abu Jaffor Morshedul Abedin (22803884) Rusab Sarmun (17632269) Adam Mushtak (22048013) Mohamed Sultan Bin Mohamed Ali (22803887) Anwarul Hasan (1332066) Ponnuthurai Nagaratnam Suganthan (11274636) Muhammad E.H. Chowdhury (17151154) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abu Jaffor Morshedul Abedin (22803884) Rusab Sarmun (17632269) Adam Mushtak (22048013) Mohamed Sultan Bin Mohamed Ali (22803887) Anwarul Hasan (1332066) Ponnuthurai Nagaratnam Suganthan (11274636) Muhammad E.H. Chowdhury (17151154) |
| dc.date.none.fl_str_mv | 2025-05-12T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.bspc.2025.108023 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhanced_coronary_artery_segmentation_and_stenosis_detection_Leveraging_novel_deep_learning_techniques/30819755 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Coronary artery disease Automated segmentation Stenosis localization Medical image analysis Deep learning |
| dc.title.none.fl_str_mv | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Coronary artery disease (CAD) is a significant global health concern, emphasizing the need for reliable and automated diagnostic solutions. This study proposes a novel <u>deep learning </u>framework aimed at improving both full artery segmentation and <u>stenosis</u> localization by incorporating Self-Organizing <u>Neural Networks</u> (Self-ONN). The DenseSelfU-Net model leverages DenseNet121 as an encoder and a Self-ONN enhanced decoder within a U-Net based architecture to achieve robust feature extraction and precise full artery segmentation, achieving an IoU of 82.52% and a Dice score of 90.35% on the ARCADE Challenge dataset. For stenosis localization, Self-ONN is integrated into key components of the Multi-Scale <u>Attention Network</u> (MA-Net), which includes the Multi-Scale Fusion Attention Block (MFAB) and the Position-wise Attention Block (PAB), capturing complex vascular patterns through both local and global dependencies and resulting in the DenseSelfMA-Net model. The DenseSelfMA-Net achieves Dice scores of 60.59% and 60.36% and IoU scores of 46.09% and 45.36% for the MFAB and <u>PAB configurations</u>, respectively on the ARCADE challenge dataset. These results demonstrate the effectiveness of Self-ONN in enhancing diagnostic precision and facilitating early CAD diagnosis, with promising implications for clinical practice.</p><h2>Other Information</h2><p dir="ltr">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.2025.108023" target="_blank">https://dx.doi.org/10.1016/j.bspc.2025.108023</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_27fefae4d6615fac7347f29d439f3fdb |
| identifier_str_mv | 10.1016/j.bspc.2025.108023 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30819755 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniquesAbu Jaffor Morshedul Abedin (22803884)Rusab Sarmun (17632269)Adam Mushtak (22048013)Mohamed Sultan Bin Mohamed Ali (22803887)Anwarul Hasan (1332066)Ponnuthurai Nagaratnam Suganthan (11274636)Muhammad E.H. Chowdhury (17151154)Biomedical and clinical sciencesCardiovascular medicine and haematologyEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningCoronary artery diseaseAutomated segmentationStenosis localizationMedical image analysisDeep learning<p dir="ltr">Coronary artery disease (CAD) is a significant global health concern, emphasizing the need for reliable and automated diagnostic solutions. This study proposes a novel <u>deep learning </u>framework aimed at improving both full artery segmentation and <u>stenosis</u> localization by incorporating Self-Organizing <u>Neural Networks</u> (Self-ONN). The DenseSelfU-Net model leverages DenseNet121 as an encoder and a Self-ONN enhanced decoder within a U-Net based architecture to achieve robust feature extraction and precise full artery segmentation, achieving an IoU of 82.52% and a Dice score of 90.35% on the ARCADE Challenge dataset. For stenosis localization, Self-ONN is integrated into key components of the Multi-Scale <u>Attention Network</u> (MA-Net), which includes the Multi-Scale Fusion Attention Block (MFAB) and the Position-wise Attention Block (PAB), capturing complex vascular patterns through both local and global dependencies and resulting in the DenseSelfMA-Net model. The DenseSelfMA-Net achieves Dice scores of 60.59% and 60.36% and IoU scores of 46.09% and 45.36% for the MFAB and <u>PAB configurations</u>, respectively on the ARCADE challenge dataset. These results demonstrate the effectiveness of Self-ONN in enhancing diagnostic precision and facilitating early CAD diagnosis, with promising implications for clinical practice.</p><h2>Other Information</h2><p dir="ltr">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.2025.108023" target="_blank">https://dx.doi.org/10.1016/j.bspc.2025.108023</a></p>2025-05-12T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.bspc.2025.108023https://figshare.com/articles/journal_contribution/Enhanced_coronary_artery_segmentation_and_stenosis_detection_Leveraging_novel_deep_learning_techniques/30819755CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308197552025-05-12T15:00:00Z |
| spellingShingle | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques Abu Jaffor Morshedul Abedin (22803884) Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Coronary artery disease Automated segmentation Stenosis localization Medical image analysis Deep learning |
| status_str | publishedVersion |
| title | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| title_full | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| title_fullStr | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| title_full_unstemmed | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| title_short | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| title_sort | Enhanced coronary artery segmentation and stenosis detection: Leveraging novel deep learning techniques |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Coronary artery disease Automated segmentation Stenosis localization Medical image analysis Deep learning |