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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| Vydané v: | SoftwareX Ročník 12; s. 100570 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
01.07.2020
Elsevier |
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| ISSN: | 2352-7110, 2352-7110 |
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| Abstract | 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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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 100570 |
| Author | Solís-Lemus, José Alonso de Vecchi, Adelaide Niederer, Steven A. Corrado, Cesare Karim, Rashed Razeghi, Orod Lee, Angela W.C. Roney, Caroline H. |
| Author_xml | – sequence: 1 givenname: Orod surname: Razeghi fullname: Razeghi, Orod email: orod.razeghi@kcl.ac.uk – sequence: 2 givenname: José Alonso surname: Solís-Lemus fullname: Solís-Lemus, José Alonso – sequence: 3 givenname: Angela W.C. surname: Lee fullname: Lee, Angela W.C. – sequence: 4 givenname: Rashed surname: Karim fullname: Karim, Rashed – sequence: 5 givenname: Cesare surname: Corrado fullname: Corrado, Cesare – sequence: 6 givenname: Caroline H. surname: Roney fullname: Roney, Caroline H. – sequence: 7 givenname: Adelaide surname: de Vecchi fullname: de Vecchi, Adelaide – sequence: 8 givenname: Steven A. surname: Niederer fullname: Niederer, Steven A. |
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| Cites_doi | 10.3389/fcvm.2020.00025 10.1016/j.media.2018.10.001 10.1016/j.cmpb.2009.09.002 10.1016/j.hrthm.2017.04.041 10.1016/j.jcct.2018.04.003 10.1109/TMI.2015.2398818 10.1186/s12968-019-0574-z 10.1109/42.796284 10.1016/j.media.2013.04.010 10.1016/j.jcmg.2019.03.027 10.1109/TMI.2009.2021652 10.1038/s41569-018-0149-y 10.1093/europace/euz226 |
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| Keywords | Computer vision Interactive platform Medical imaging Cardiovascular research Image processing Machine learning |
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| Title | CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research |
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