Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping

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dc.authorscopusid24485414800en_US
dc.authorwosidHKE-7359-2023en_US
dc.contributor.authorHann, Evan
dc.contributor.authorPopescu, Iulia A.
dc.contributor.authorZhang, Qiang
dc.contributor.authorGonzales, Ricardo A.
dc.contributor.authorBarutcu, Ahmet
dc.date.accessioned2025-02-10T08:17:25Z
dc.date.available2025-02-10T08:17:25Z
dc.date.issued2021en_US
dc.departmentFakülteler, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümüen_US
dc.description.abstractRecent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conclusions, diagnoses or even harm to patients. Manual inspection for potential inaccuracies is labor-intensive and time-consuming, hampering progress towards fast and accurate clinical reporting in high volumes. To promote reliable fully-automated image analysis, we propose a quality control-driven (QCD) segmentation framework. It is an ensemble of neural networks that integrate image analysis and quality control. The novelty of this framework is the selection of the most optimal segmentation based on predicted segmentation accuracy, on-the-fly. Additionally, this framework visualizes segmentation agreement to provide traceability of the quality control process. In this work, we demonstrated the utility of the framework in cardiovascular magnetic resonance T1-mapping - a quantitative technique for myocardial tissue characterization. The framework achieved near-perfect agreement with expert image analysts in estimating myocardial T1 value (r=0.987, p<.0005; mean absolute error (MAE)=11.3ms), with accurate segmentation quality prediction (Dice coefficient prediction MAE=0.0339) and classification (accuracy=0.99), and a fast average processing time of 0.39 second/image. In summary, the QCD framework can generate high-throughput automated image analysis with speed and accuracy that is highly desirable for large-scale clinical applications.en_US
dc.identifier.citationHann, E., Popescu, I. A., Zhang, Q., Gonzales, R. A., Barutçu, A., Neubauer, S., … Piechnik, S. K. (2021). Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping. Medical Image Analysis, 71, 102029. https://doi.org/10.1016/j.media.2021.102029en_US
dc.identifier.doi10.1016/j.media.2021.102029en_US
dc.identifier.issn1361-8415 / 1361-8423
dc.identifier.pmidPMID: 33831594en_US
dc.identifier.scopus2-s2.0-85103695127en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.media.2021.102029
dc.identifier.urihttps://hdl.handle.net/20.500.12428/29606
dc.identifier.volume71en_US
dc.identifier.wosWOS:000663615600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorBarutcu, Ahmet
dc.institutionauthorid0000-0002-3562-2075
dc.language.isoengen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofMedical Image Analysisen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectCardiovascular MRIen_US
dc.subjectEnsemble neural networken_US
dc.subjectImage quality assessmenten_US
dc.subjectSegmentationen_US
dc.titleDeep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mappingen_US
dc.typearticleen_US

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