Study of CNN Capacity Applied to Left Ventricle Segmentation in Cardiac MRI

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art_TOLEDO_Study_of_CNN_Capacity_Applied_to_Left_Ventricle_2021.PDF (1.66 MB)
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0
Tipo de produção
article
Data de publicação
2021
DOI
Scopus
Título da Revista
ISSN da Revista
Título do Volume
Editora
SPRINGER
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Citação
SN COMPUTER SCIENCE, v.2, n.6, article ID 480, p, 2021
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Unidades Organizacionais
Fascículo
Resumo
CNN (Convolutional Neural Network) models have been successfully used for segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance Imaging), providing clinical measurements. In practice, two questions arise with deployment of CNNs: (1) when is it better to use a shallow model instead of a deeper one? (2) how the size of a dataset might change the network performance? We propose a framework to answer them, by experimenting with deep and shallow versions of three U-Net families, trained from scratch in six subsets varying from 100 to 10,000 images, different network sizes, learning rates and regularization values. 1620 models were evaluated using five-fold cross-validation by loss and DICE. The results indicate that: sample size affects performance more than architecture or hyper-parameters; in small samples the performance is more sensitive to hyper-parameters than architecture; the performance difference between shallow and deeper networks is not the same across families. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Palavras-chave
Cardiac, Deep learning, Magnetic resonance imaging, Model selection, Segmentation
URI
https://observatorio.fm.usp.br/handle/OPI/49010
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Artigos e Materiais de Revistas Científicas - FM/MCP
Artigos e Materiais de Revistas Científicas - HC/InCor
Artigos e Materiais de Revistas Científicas - LIM/13
Artigos e Materiais de Revistas Científicas - LIM/65