Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View

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dc.authorid0000-0003-2236-7279
dc.authorid0000-0001-8808-2714
dc.authorid0000-0002-1467-8176
dc.contributor.authorVimalesvaran, Kavitha
dc.contributor.authorUslu, Fatmatulzehra
dc.contributor.authorZaman, Sameer
dc.contributor.authorGalazis, Christoforos
dc.contributor.authorHoward, James
dc.contributor.authorCole, Graham
dc.contributor.authorBharath, Anil A.
dc.date.accessioned2026-02-12T21:05:13Z
dc.date.available2026-02-12T21:05:13Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) -- SEP 18-22, 2022 -- Singapore, SINGAPORE
dc.description.abstractCardiac magnetic resonance (CMR) is the gold standard for quantification of cardiac volumes, function, and blood flow. Tailored MR pulse sequences define the contrast mechanisms, acquisition geometry and timing which can be applied during CMR to achieve unique tissue characterisation. It is impractical for each patient to have every possible acquisition option. We target the aortic valve in the three-chamber (3-CH) cine CMR view. Two major types of anomalies are possible in the aortic valve. Stenosis: the narrowing of the valve which prevents an adequate outflow of blood, and insufficiency (regurgitation): the inability to stop the back-flow of blood into the left ventricle. We develop and evaluate a deep learning system to accurately classify aortic valve abnormalities to enable further directed imaging for patients who require it. Inspired by low level image processing tasks, we propose a multi-level network that generates heat maps to locate the aortic valve leaflets' hinge points and aortic stenosis or regurgitation jets. We trained and evaluated all our models on a dataset of clinical CMR studies obtained from three NHS hospitals (n = 1,017 patients). Our results (mean accuracy = 0.93 and F1 score = 0.91), show that an expert-guided deep learning-based feature extraction and a classification model provide a feasible strategy for prescribing further, directed imaging, thus improving the efficiency and utility of CMR scanning.
dc.description.sponsorshipUKRI CDT in AI for Healthcare [EP/S023283/1]
dc.description.sponsorshipMICCAI Soc
dc.description.sponsorshipThis work was supported by the UKRI CDT in AI for Healthcare http://ai4health.io (Grant No. EP/S023283/1)
dc.identifier.doi10.1007/978-3-031-16431-6_54
dc.identifier.endpage580
dc.identifier.isbn978-3-031-16431-6
dc.identifier.isbn978-3-031-16430-9
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-85138834454
dc.identifier.scopusqualityQ3
dc.identifier.startpage571
dc.identifier.urihttps://doi.org/10.1007/978-3-031-16431-6_54
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6855
dc.identifier.volume13431
dc.identifier.wosWOS:000867524300054
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofMedical Image Computing and Computer Assisted Intervention, Miccai 2022, Pt I
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectCardiac MRI
dc.subjectAortic valve
dc.subjectExplainability
dc.subjectMachine learning
dc.titleDetecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View
dc.typeConference Object

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