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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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Detalles Bibliográficos
Autores principales: Razeghi, Orod, Solís-Lemus, José Alonso, Lee, Angela W.C., Karim, Rashed, Corrado, Cesare, Roney, Caroline H., de Vecchi, Adelaide, Niederer, Steven A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610963/
https://www.ncbi.nlm.nih.gov/pubmed/34124331
http://dx.doi.org/10.1016/j.softx.2020.100570
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author Razeghi, Orod
Solís-Lemus, José Alonso
Lee, Angela W.C.
Karim, Rashed
Corrado, Cesare
Roney, Caroline H.
de Vecchi, Adelaide
Niederer, Steven A.
author_facet Razeghi, Orod
Solís-Lemus, José Alonso
Lee, Angela W.C.
Karim, Rashed
Corrado, Cesare
Roney, Caroline H.
de Vecchi, Adelaide
Niederer, Steven A.
author_sort Razeghi, Orod
collection PubMed
description 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.
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spelling pubmed-76109632021-06-11 CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research Razeghi, Orod Solís-Lemus, José Alonso Lee, Angela W.C. Karim, Rashed Corrado, Cesare Roney, Caroline H. de Vecchi, Adelaide Niederer, Steven A. SoftwareX Article 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. 2020-07-31 /pmc/articles/PMC7610963/ /pubmed/34124331 http://dx.doi.org/10.1016/j.softx.2020.100570 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Razeghi, Orod
Solís-Lemus, José Alonso
Lee, Angela W.C.
Karim, Rashed
Corrado, Cesare
Roney, Caroline H.
de Vecchi, Adelaide
Niederer, Steven A.
CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
title CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
title_full CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
title_fullStr CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
title_full_unstemmed CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
title_short CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
title_sort cemrgapp: an interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610963/
https://www.ncbi.nlm.nih.gov/pubmed/34124331
http://dx.doi.org/10.1016/j.softx.2020.100570
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