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MITEA: a dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging

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dc.contributor.author Zhao, Debbie
dc.contributor.author Ferdian, Edward
dc.contributor.author Maso Talou, Gonzalo Daniel
dc.contributor.author Quill, Gina M.
dc.contributor.author Gilbert, Kathleen
dc.contributor.author Wang, Vicky Y.
dc.contributor.author Babarenda Gamage, Thiranja Prasad
dc.contributor.author Pedrosa, João
dc.contributor.author D’hooge, Jan
dc.contributor.author Sutton, Timothy M.
dc.contributor.author Lowe, Boris S.
dc.contributor.author Legget, Malcolm E.
dc.contributor.author Ruygrok, Peter N.
dc.contributor.author Doughty, Robert N.
dc.contributor.author Camara, Oscar
dc.contributor.author Young, Alistair A.
dc.contributor.author Nash, Martyn P.
dc.date.accessioned 2023-03-21T07:05:48Z
dc.date.available 2023-03-21T07:05:48Z
dc.date.issued 2023
dc.identifier.citation Zhao D, Ferdian E, Maso GD, Quill GM, Gilbert K, Wang VY, et al. MITEA: a dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging. Front Cardiovasc Med. 2023 Jan 10;9:1016703. DOI: 10.3389/fcvm.2022.1016703
dc.identifier.issn 2297-055X
dc.identifier.uri http://hdl.handle.net/10230/56292
dc.description.abstract Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of −9 ± 16 ml, −1 ± 10 ml, −2 ± 5 %, and 5 ± 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Frontiers in Cardiovascular Medicine. 2023 Jan 10;9:1016703
dc.rights © 2023 Zhao, Ferdian, Maso Talou, Quill, Gilbert, Wang, Babarenda Gamage, Pedrosa, D’hooge, Sutton, Lowe, Legget, Ruygrok, Doughty, Camara, Young and Nash. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title MITEA: a dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fcvm.2022.1016703
dc.subject.keyword 3D echocardiography (3DE)
dc.subject.keyword Machine learning (ML)
dc.subject.keyword Segmentation (image processing)
dc.subject.keyword Left ventricle (LV)
dc.subject.keyword Multimodal imaging
dc.subject.keyword Cardiac magnetic resonance (CMR) imaging
dc.subject.keyword Domain adaptation
dc.subject.keyword Cardiac Atlas Project
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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