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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Title

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

Content partner

The University of Auckland Library

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ResearchSpace@Auckland

Description

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 no...

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Format

Research paper

Research format

Journal article

Date created

2022-01

Creator

Zhao, Debbie / Ferdian, Edward / Maso Talou, Gonzalo D / Quill, Gina M / Gilbert, Kathleen / Wang, Vicky Y / Babarenda Gamage, Thiranja P / Pedrosa, João / D'hooge, Jan / Sutton, Timothy M / Lowe, Boris S / Legget, Malcolm E / Ruygrok, Peter N / Doughty, Robert N / Camara, Oscar / Young, Alistair A / Nash, Martyn P

URL

https://hdl.handle.net/2292/63327

Related subjects

3D echocardiography (3DE) / Cardiac Atlas Project / cardiac magnetic resonance (CMR) imaging / domain adaptation / left ventricle (LV) / machine learning (ML) / multimodal imaging / segmentation (image processing) / Clinical Research / Cardiovascular / Biomedical Imaging / Bioengineering / Heart Disease

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