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dc.contributor.authorHu, Jieyu
dc.contributor.authorOlaisen, Sindre Hellum
dc.contributor.authorSmistad, Erik
dc.contributor.authorDalen, Håvard
dc.contributor.authorLøvstakken, Lasse
dc.date.accessioned2024-05-31T08:09:40Z
dc.date.available2024-05-31T08:09:40Z
dc.date.created2023-10-20T12:48:24Z
dc.date.issued2023
dc.identifier.citationUltrasound in Medicine and Biology. 2023, 50 (1), 47-56.en_US
dc.identifier.issn0301-5629
dc.identifier.urihttps://hdl.handle.net/11250/3132064
dc.description.abstractObjective: Echocardiography, a critical tool for assessing left atrial (LA) volume, often relies on manual or semi-automated measurements. This study introduces a fully automated, real-time method for measuring LA volume in both 2-D and 3-D imaging, in the aim of offering accuracy comparable to that of expert assessments while saving time and reducing operator variability. Methods: We developed an automated pipeline comprising a network to identify the end-systole (ES) time point and robust 2-D and 3-D U-Nets for segmentation. We employed data sets of 789 2-D images and 286 3-D recordings and explored various training regimes, including recurrent networks and pseudo-labeling, to estimate volume curves. Results: Our baseline results revealed an average volume difference of 2.9 mL for 2-D and 7.8 mL for 3-D, respectively, compared with manual methods. The application of pseudo-labeling to all frames in the cine loop generally led to more robust volume curves and notably improved ES measurement in cases with limited data. Conclusion: Our results highlight the potential of automated LA volume estimation in clinical practice. The proposed prototype application, capable of processing real-time data from a clinical ultrasound scanner, provides valuable temporal volume curve information in the echo lab.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAutomated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learningen_US
dc.title.alternativeAutomated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learningen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber47-56en_US
dc.source.volume50en_US
dc.source.journalUltrasound in Medicine and Biologyen_US
dc.source.issue1en_US
dc.identifier.doi10.1016/j.ultrasmedbio.2023.08.024
dc.identifier.cristin2186748
dc.relation.projectNorges forskningsråd: 237887en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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