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

TY - JOUR

T1 - CemrgApp

T2 - An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research

AU - Razeghi, Orod

AU - Solís-Lemus, José Alonso

AU - Lee, Angela W.C.

AU - Karim, Rashed

AU - Corrado, Cesare

AU - Roney, Caroline H.

AU - de Vecchi, Adelaide

AU - Niederer, Steven A.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - 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.

AB - 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.

KW - Cardiovascular research

KW - Computer vision

KW - Image processing

KW - Interactive platform

KW - Machine learning

KW - Medical imaging

UR - http://www.scopus.com/inward/record.url?scp=85088895187&partnerID=8YFLogxK

U2 - 10.1016/j.softx.2020.100570

DO - 10.1016/j.softx.2020.100570

M3 - Article

AN - SCOPUS:85088895187

SN - 2352-7110

VL - 12

JO - SoftwareX

JF - SoftwareX

M1 - 100570

ER -