UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a c...
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Autor*in:	Elizaveta Lavrova [verfasserIn] Zohaib Salahuddin [verfasserIn] Henry C. Woodruff [verfasserIn] Mohamed Kassem [verfasserIn] Robin Camarasa [verfasserIn] Anja G. Der Van Kolk [verfasserIn] Paul J. Nederkoorn [verfasserIn] Daniel Bos [verfasserIn] Jeroen Hendrikse [verfasserIn] M. Eline Kooi [verfasserIn] Philippe Lambin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 26637-26651
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:26637-26651

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3258408

Katalog-ID:	DOAJ087806398

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allfields	10.1109/ACCESS.2023.3258408 doi (DE-627)DOAJ087806398 (DE-599)DOAJ3b30660a7ca54aec9bb756996e7c2b2b DE-627 ger DE-627 rakwb eng TK1-9971 Elizaveta Lavrova verfasserin aut UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation. Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization Electrical engineering. Electronics. Nuclear engineering Zohaib Salahuddin verfasserin aut Henry C. Woodruff verfasserin aut Mohamed Kassem verfasserin aut Robin Camarasa verfasserin aut Anja G. Der Van Kolk verfasserin aut Paul J. Nederkoorn verfasserin aut Daniel Bos verfasserin aut Jeroen Hendrikse verfasserin aut M. Eline Kooi verfasserin aut Philippe Lambin verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 26637-26651 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:26637-26651 https://doi.org/10.1109/ACCESS.2023.3258408 kostenfrei https://doaj.org/article/3b30660a7ca54aec9bb756996e7c2b2b kostenfrei https://ieeexplore.ieee.org/document/10073528/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 26637-26651
spelling	10.1109/ACCESS.2023.3258408 doi (DE-627)DOAJ087806398 (DE-599)DOAJ3b30660a7ca54aec9bb756996e7c2b2b DE-627 ger DE-627 rakwb eng TK1-9971 Elizaveta Lavrova verfasserin aut UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation. Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization Electrical engineering. Electronics. Nuclear engineering Zohaib Salahuddin verfasserin aut Henry C. Woodruff verfasserin aut Mohamed Kassem verfasserin aut Robin Camarasa verfasserin aut Anja G. Der Van Kolk verfasserin aut Paul J. Nederkoorn verfasserin aut Daniel Bos verfasserin aut Jeroen Hendrikse verfasserin aut M. Eline Kooi verfasserin aut Philippe Lambin verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 26637-26651 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:26637-26651 https://doi.org/10.1109/ACCESS.2023.3258408 kostenfrei https://doaj.org/article/3b30660a7ca54aec9bb756996e7c2b2b kostenfrei https://ieeexplore.ieee.org/document/10073528/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 26637-26651
allfields_unstemmed	10.1109/ACCESS.2023.3258408 doi (DE-627)DOAJ087806398 (DE-599)DOAJ3b30660a7ca54aec9bb756996e7c2b2b DE-627 ger DE-627 rakwb eng TK1-9971 Elizaveta Lavrova verfasserin aut UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation. Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization Electrical engineering. Electronics. Nuclear engineering Zohaib Salahuddin verfasserin aut Henry C. Woodruff verfasserin aut Mohamed Kassem verfasserin aut Robin Camarasa verfasserin aut Anja G. Der Van Kolk verfasserin aut Paul J. Nederkoorn verfasserin aut Daniel Bos verfasserin aut Jeroen Hendrikse verfasserin aut M. Eline Kooi verfasserin aut Philippe Lambin verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 26637-26651 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:26637-26651 https://doi.org/10.1109/ACCESS.2023.3258408 kostenfrei https://doaj.org/article/3b30660a7ca54aec9bb756996e7c2b2b kostenfrei https://ieeexplore.ieee.org/document/10073528/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 26637-26651
allfieldsGer	10.1109/ACCESS.2023.3258408 doi (DE-627)DOAJ087806398 (DE-599)DOAJ3b30660a7ca54aec9bb756996e7c2b2b DE-627 ger DE-627 rakwb eng TK1-9971 Elizaveta Lavrova verfasserin aut UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation. Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization Electrical engineering. Electronics. Nuclear engineering Zohaib Salahuddin verfasserin aut Henry C. Woodruff verfasserin aut Mohamed Kassem verfasserin aut Robin Camarasa verfasserin aut Anja G. Der Van Kolk verfasserin aut Paul J. Nederkoorn verfasserin aut Daniel Bos verfasserin aut Jeroen Hendrikse verfasserin aut M. Eline Kooi verfasserin aut Philippe Lambin verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 26637-26651 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:26637-26651 https://doi.org/10.1109/ACCESS.2023.3258408 kostenfrei https://doaj.org/article/3b30660a7ca54aec9bb756996e7c2b2b kostenfrei https://ieeexplore.ieee.org/document/10073528/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 26637-26651
allfieldsSound	10.1109/ACCESS.2023.3258408 doi (DE-627)DOAJ087806398 (DE-599)DOAJ3b30660a7ca54aec9bb756996e7c2b2b DE-627 ger DE-627 rakwb eng TK1-9971 Elizaveta Lavrova verfasserin aut UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation. Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization Electrical engineering. Electronics. Nuclear engineering Zohaib Salahuddin verfasserin aut Henry C. Woodruff verfasserin aut Mohamed Kassem verfasserin aut Robin Camarasa verfasserin aut Anja G. Der Van Kolk verfasserin aut Paul J. Nederkoorn verfasserin aut Daniel Bos verfasserin aut Jeroen Hendrikse verfasserin aut M. Eline Kooi verfasserin aut Philippe Lambin verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 26637-26651 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:26637-26651 https://doi.org/10.1109/ACCESS.2023.3258408 kostenfrei https://doaj.org/article/3b30660a7ca54aec9bb756996e7c2b2b kostenfrei https://ieeexplore.ieee.org/document/10073528/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 26637-26651
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callnumber-first	T - Technology
author	Elizaveta Lavrova
spellingShingle	Elizaveta Lavrova misc TK1-9971 misc Auto-segmentation misc carotid MRI misc vessel segmentation misc U-Net misc uncertainty regularization misc Electrical engineering. Electronics. Nuclear engineering UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images
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topic_title	TK1-9971 UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images Auto-segmentation carotid MRI vessel segmentation U-Net uncertainty regularization
topic	misc TK1-9971 misc Auto-segmentation misc carotid MRI misc vessel segmentation misc U-Net misc uncertainty regularization misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Auto-segmentation misc carotid MRI misc vessel segmentation misc U-Net misc uncertainty regularization misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Auto-segmentation misc carotid MRI misc vessel segmentation misc U-Net misc uncertainty regularization misc Electrical engineering. Electronics. Nuclear engineering
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title	UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images
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title_full	UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images
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title_sort	ur-cara-net: a cascaded framework with uncertainty regularization for automated segmentation of carotid arteries on black blood mr images
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title_auth	UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images
abstract	We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation.
abstractGer	We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation.
abstract_unstemmed	We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation.
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title_short	UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images
url	https://doi.org/10.1109/ACCESS.2023.3258408 https://doaj.org/article/3b30660a7ca54aec9bb756996e7c2b2b https://ieeexplore.ieee.org/document/10073528/ https://doaj.org/toc/2169-3536
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Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Auto-segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">carotid MRI</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">vessel segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">U-Net</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">uncertainty regularization</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zohaib Salahuddin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Henry C. Woodruff</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohamed Kassem</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Robin Camarasa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Anja G. Der Van Kolk</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paul J. 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UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

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