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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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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. |
format | Online Article Text |
id | pubmed-7610963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
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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