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Article

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

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Department of Automation, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania
2
Department of Applied Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
3
Department of Mathematical Modelling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Academic Editor: Arcangelo Merla
Sensors 2021, 21(3), 863; https://doi.org/10.3390/s21030863
Received: 26 November 2020 / Revised: 22 January 2021 / Accepted: 25 January 2021 / Published: 28 January 2021
(This article belongs to the Special Issue Sensors for Biomedical Imaging)
Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image. View Full-Text
Keywords: image fusion; multi-focus; embryo development; data reduction; deep learning; convolutional neural networks; laplacian pyramid; correlation coefficient maximization image fusion; multi-focus; embryo development; data reduction; deep learning; convolutional neural networks; laplacian pyramid; correlation coefficient maximization
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MDPI and ACS Style

Raudonis, V.; Paulauskaite-Taraseviciene, A.; Sutiene, K. Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement. Sensors 2021, 21, 863. https://doi.org/10.3390/s21030863

AMA Style

Raudonis V, Paulauskaite-Taraseviciene A, Sutiene K. Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement. Sensors. 2021; 21(3):863. https://doi.org/10.3390/s21030863

Chicago/Turabian Style

Raudonis, Vidas, Agne Paulauskaite-Taraseviciene, and Kristina Sutiene. 2021. "Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement" Sensors 21, no. 3: 863. https://doi.org/10.3390/s21030863

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