Please use this identifier to cite or link to this item: https://observatorio.fm.usp.br/handle/OPI/40291
Title: Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks
Authors: PEREIRA, Renato F.REBELO, Marina S.MORENO, Ramon A.MARCO, Anderson G.LIMA, Daniel M.ARRUDA, Marcelo A. F.KRIEGER, Jose E.GUTIERREZ, Marco A.
Citation: 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, p.1221-1224, 2020
Abstract: Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction.
Appears in Collections:

Comunicações em Eventos - FM/MCP
Departamento de Cardio-Pneumologia - FM/MCP

Comunicações em Eventos - HC/InCor
Instituto do Coração - HC/InCor

Comunicações em Eventos - LIM/13
LIM/13 - Laboratório de Genética e Cardiologia Molecular

Comunicações em Eventos - LIM/65
LIM/65 - Laboratório de Investigação Médica em Bioengenharia


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