CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research

Personalised medicine is based on the principle that each body is unique and will respond to therapies differently. In cardiology, characterising patient specific cardiovascular properties would help in personalising care. One promising approach for characterising these properties relies on performi...

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Veröffentlicht in:SoftwareX Jg. 12; S. 100570
Hauptverfasser: Razeghi, Orod, Solís-Lemus, José Alonso, Lee, Angela W.C., Karim, Rashed, Corrado, Cesare, Roney, Caroline H., de Vecchi, Adelaide, Niederer, Steven A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.07.2020
Elsevier
Schlagworte:
Cardiovascular research
Computer vision
Image processing
Interactive platform
Machine learning
Medical imaging
Computer vision
Interactive platform
Medical imaging
Cardiovascular research
Image processing
Machine learning
ISSN:2352-7110, 2352-7110
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Zusammenfassung:Personalised medicine is based on the principle that each body is unique and will respond to therapies differently. In cardiology, characterising patient specific cardiovascular properties would help in personalising care. One promising approach for characterising these properties relies on performing computational analysis of multimodal imaging data. An interactive cardiac imaging environment, which can seamlessly render, manipulate, derive calculations, and otherwise prototype research activities, is therefore sought-after. We developed the Cardiac Electro-Mechanics Research Group Application (CemrgApp) as a platform with custom image processing and computer vision toolkits for applying statistical, machine learning and simulation approaches to study physiology, pathology, diagnosis and treatment of the cardiovascular system. CemrgApp provides an integrated environment, where cardiac data visualisation and workflow prototyping are presented through a common graphical user interface.
Bibliographie:ObjectType-Article-1
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ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2020.100570