MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss
<p>Carotid artery stenosis risk stratification is one of the most sought-after methods for diagnosing the chances of stroke. There is an inherent requirement to identify the risk before its onset through techniques such as ultrasound imaging. The carotid artery intima-media thickness, a marker...
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2025
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| _version_ | 1864513555145949184 |
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| author | Najmath Ottakath (17430912) |
| author2 | Younes Akbari (16303286) Somaya Al Maadeed (20090823) Mohammad E.H. Chowdhury (20090826) Susu Zughaier (367245) Ahmed Bouridane (2270131) Kishor Kumar Sadasivuni (8036039) |
| author2_role | author author author author author author |
| author_facet | Najmath Ottakath (17430912) Younes Akbari (16303286) Somaya Al Maadeed (20090823) Mohammad E.H. Chowdhury (20090826) Susu Zughaier (367245) Ahmed Bouridane (2270131) Kishor Kumar Sadasivuni (8036039) |
| author_role | author |
| dc.creator.none.fl_str_mv | Najmath Ottakath (17430912) Younes Akbari (16303286) Somaya Al Maadeed (20090823) Mohammad E.H. Chowdhury (20090826) Susu Zughaier (367245) Ahmed Bouridane (2270131) Kishor Kumar Sadasivuni (8036039) |
| dc.date.none.fl_str_mv | 2025-02-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.bspc.2024.107077 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MSEUnet_Refined_Intima-media_segmentation_of_the_carotid_artery_based_on_a_multi-scale_approach_using_patch-wise_dice_loss/27574602 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Machine learning Carotid Artery intima-media Medical image segmentation Multi-scale squeeze and excite Unet Patch-wise dice loss function |
| dc.title.none.fl_str_mv | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Carotid artery stenosis risk stratification is one of the most sought-after methods for diagnosing the chances of stroke. There is an inherent requirement to identify the risk before its onset through techniques such as ultrasound imaging. The carotid artery intima-media thickness, a marker for stenosis, can be identified, marked, and assessed. Typically performed by a trained operator, now automated approaches have been introduced that can automatically segment and classify the status of the carotid artery intima-media, aiding in the diagnosis of the chances of stroke. In this paper, a new framework based on two components is presented to segment the intima-media layer of the carotid artery to aid in diagnosis of the status. Firstly, the segmentation model is based on an enhanced Unet using multi-scale squeeze and excite operations. Secondly, a novel patch-wise dice loss function is introduced to optimize the normal dice loss function. The obtained results using augmentation on two combined datasets indicate an improvement in different metrics with respect to the state of the art. Notably, 89.4% dice coefficient index and 80.85% IoU, with data augmentation. The source code for the functions discussed in this paper will be available at https://github.com/Vlabgit/MSEUnet.git.</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.2024.107077" target="_blank">https://dx.doi.org/10.1016/j.bspc.2024.107077</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_36b980183d1aadf987a722443502fb22 |
| identifier_str_mv | 10.1016/j.bspc.2024.107077 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27574602 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice lossNajmath Ottakath (17430912)Younes Akbari (16303286)Somaya Al Maadeed (20090823)Mohammad E.H. Chowdhury (20090826)Susu Zughaier (367245)Ahmed Bouridane (2270131)Kishor Kumar Sadasivuni (8036039)EngineeringBiomedical engineeringInformation and computing sciencesMachine learningCarotid Artery intima-mediaMedical image segmentationMulti-scale squeeze and excite UnetPatch-wise dice loss function<p>Carotid artery stenosis risk stratification is one of the most sought-after methods for diagnosing the chances of stroke. There is an inherent requirement to identify the risk before its onset through techniques such as ultrasound imaging. The carotid artery intima-media thickness, a marker for stenosis, can be identified, marked, and assessed. Typically performed by a trained operator, now automated approaches have been introduced that can automatically segment and classify the status of the carotid artery intima-media, aiding in the diagnosis of the chances of stroke. In this paper, a new framework based on two components is presented to segment the intima-media layer of the carotid artery to aid in diagnosis of the status. Firstly, the segmentation model is based on an enhanced Unet using multi-scale squeeze and excite operations. Secondly, a novel patch-wise dice loss function is introduced to optimize the normal dice loss function. The obtained results using augmentation on two combined datasets indicate an improvement in different metrics with respect to the state of the art. Notably, 89.4% dice coefficient index and 80.85% IoU, with data augmentation. The source code for the functions discussed in this paper will be available at https://github.com/Vlabgit/MSEUnet.git.</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.2024.107077" target="_blank">https://dx.doi.org/10.1016/j.bspc.2024.107077</a></p>2025-02-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.bspc.2024.107077https://figshare.com/articles/journal_contribution/MSEUnet_Refined_Intima-media_segmentation_of_the_carotid_artery_based_on_a_multi-scale_approach_using_patch-wise_dice_loss/27574602CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/275746022025-02-01T00:00:00Z |
| spellingShingle | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss Najmath Ottakath (17430912) Engineering Biomedical engineering Information and computing sciences Machine learning Carotid Artery intima-media Medical image segmentation Multi-scale squeeze and excite Unet Patch-wise dice loss function |
| status_str | publishedVersion |
| title | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| title_full | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| title_fullStr | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| title_full_unstemmed | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| title_short | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| title_sort | MSEUnet: Refined Intima-media segmentation of the carotid artery based on a multi-scale approach using patch-wise dice loss |
| topic | Engineering Biomedical engineering Information and computing sciences Machine learning Carotid Artery intima-media Medical image segmentation Multi-scale squeeze and excite Unet Patch-wise dice loss function |