Exploring different Convolutional Neural Networks architectures to identify cells in spheroids
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2020
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BRAZILIAN CONGRESS IN BIOMEDICAL ENGINEERING, 27th
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Resumo
The cultivation of cells in 3D has gained more
interest in research once 3D architecture can be closer to full
cell physiological functionality. The cultivation of the cells in a
spheroid format has shown very promising results, further for
bioprinting developing so fast during the last decade. The interaction
of spheroids and the matrix, or bioink, have proportionate
new structures to be analyzed, specially if one would like
to follow the whole system (spheroid and bioink) without fluorescent
dyes. Trying to solve this image limitation, the aim of
this paper is to present a study on different Convolutional Neural
Networks (CNN) architectures employed to identify different
structures in fibroblast NIH-3T3 spheroids. Three different architectures
were considered: GoogleNet, ResNet18 and AlexNet,
all implemented in Python 3.7 using the PyTorch Application Interface
Programming (API). Given a spheroid image taken in a
light microscope, four structures can be identified: the cell, the
dead cell, the impurity/contamination and the background consisting
of a gel in which the spheroid is immersed. All four CNN
architectures were trained and evaluated with a dataset consisting
of over 370 samples, split into a training set (≈ 70%), a test
set (≈ 20%) and a validation set (≈ 10%). Since our dataset has
unbalanced classes, a data augmentation was applied in order
to provide a comparable number of samples for all classes being
considered.
Como referenciar
SANTIAGO, A.G.; CAMPOS, C.S.; MACEDO, M.M.G.; DAGUANO, J.K.M.B.; DERNOWSEK, J.A.; RODAS, A.C.D. Exploring different Convolutional Neural Networks architectures to identify cells in spheroids. In: BRAZILIAN CONGRESS IN BIOMEDICAL ENGINEERING, 27th, October 26-30, 2020, Vitória, ES. Proceedings... Rio de Janeiro, RJ: Sociedade Brasileira de Engenharia Biomédica, 2020. p. 2256-2260. Disponível em: http://200.136.52.105/handle/123456789/31684. Acesso em: 30 Dec 2025.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.