LA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atrium

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dc.authorid0000-0001-7153-7583en_US
dc.authorscopusid57204017683en_US
dc.contributor.authorUslu, Fatmatülzehra
dc.contributor.authorVarela, Marta
dc.contributor.authorBoniface, Georgia
dc.contributor.authorMahenthran, Thakshayene
dc.contributor.authorChubb, Henry
dc.contributor.authorBharath, Anil A.
dc.date.accessioned2022-04-21T06:04:15Z
dc.date.available2022-04-21T06:04:15Z
dc.date.issued2022en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAlthough atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.en_US
dc.identifier.doi10.1109/TMI.2021.3117495en_US
dc.identifier.endpage464en_US
dc.identifier.issn02780062
dc.identifier.issue2en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage456en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1932
dc.identifier.volume41en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorUslu, Fatmatülzehra
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Transactions on Medical Imagingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCardiac MRIen_US
dc.subjectEdge detectionen_US
dc.subjectImage segmentationen_US
dc.subjectSqueeze-excitation networksen_US
dc.subjectU-Net; à trous convolutionen_US
dc.titleLA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atriumen_US
dc.typeArticleen_US

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