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DC poleHodnotaJazyk
dc.contributor.authorSchmidt, Christian
dc.contributor.authorOverhoff, Heinrich Martin
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T10:51:33Z
dc.date.available2022-09-01T10:51:33Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 164-171.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49591
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectsegmentace lékařského obrazucs
dc.subjectechokardiografiecs
dc.subjecthluboké učenícs
dc.subjectU-Netcs
dc.subjectmitrální chlopeňcs
dc.titleEstimation of mitral valve hinge point coordinates - deep neural net for echocardiogram segmentationen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedCardiac image segmentation is a powerful tool in regard to diagnostics and treatment of cardiovascular diseases. Purely feature-based detection of anatomical structures like the mitral valve is a laborious task due to specifically required feature engineering and is especially challenging in echocardiograms, because of their inherently low contrast and blurry boundaries between some anatomical structures. With the publication of further annotated medical datasets and the increase in GPU processing power, deep learning-based methods in medical image segmentation became more feasible in the past years. We propose a fully automatic detection method for mitral valve hinge points, which uses a U-Net based deep neural net to segment cardiac chambers in echocardiograms in a first step, and subsequently extracts the mitral valve hinge points from the resulting segmentations in a second step. Results measured with this automatic detection method were compared to reference coordinate values, which with median absolute hinge point coordinate errors of 1.35 mm for the x- (15-85 percentile range: [0.3 mm; 3.15 mm]) and 0.75 mm for the y- coordinate (15-85 percentile range: [0.15 mm; 1.88 mm]).en
dc.subject.translatedmedical image segmentationen
dc.subject.translatedechocardiographyen
dc.subject.translateddeep learningen
dc.subject.translatedU-Neten
dc.subject.translatedmitral valveen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.21
dc.type.statusPeer-revieweden
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