Deep Learning for Multimodel Cardiac MR Image Analysis and Quantification
Deep Learning for Multi-modal Cardiac MR Image Analysis and Quantification
(Third Party Funds Single)
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Start date: January 1, 2017
End date: May 1, 2020
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Funding source: Deutscher Akademischer Austauschdienst (DAAD)
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Abstract
Cardiovascular diseases (CVDs) and other cardiac pathologies are the leading cause of death in Europe and the USA. Timely diagnosis and post-treatment follow-ups are imperative for improving survival rates and delivering high-quality patient care. These steps rely heavily on numerous cardiac imaging modalities, which include CT (computerized tomography), coronary angiography and cardiac MRI. Cardiac MRI is a non-invasive imaging modality used to detect and monitor cardiovascular diseases. Consequently, quantitative assessment and analysis of cardiac images is vital for diagnosis and devising suitable treatments. The reliability of quantitative metrics that characterize cardiac functions such as, myocardial deformation and ventricular ejection fraction, depends heavily on the precision of the heart chamber segmentation and quantification. In this project, we aim to investigate deep learning methods to improve the diagnosis and prognosis for CVDs,
Publications
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
In: STACOM 2019 2019
DOI: 10.1007/978-3-030-39074-7_32
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Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
International Workshop on Statistical Atlases and Computational Models of the Heart
In: STACOM 2018: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges 2019
DOI: 10.1007/978-3-030-12029-0_35
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Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks
In: Journal of Imaging 6 (2020), Article No.: 65
ISSN: 2313-433X
DOI: 10.3390/jimaging6070065
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Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
In: IEEE Journal of Biomedical and Health Informatics (2020)
ISSN: 2168-2194
DOI: 10.1109/JBHI.2020.3046449
URL: https://ieeexplore.ieee.org/document/9302580
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