Vidas Raudonis1, Agne Paulauskaite-Taraseviciene2, Kristina Sutiene3. 1. 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.
Abstract
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.
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.
Entities:
Keywords:
convolutional neural networks; correlation coefficient maximization; data reduction; deep learning; embryo development; image fusion; laplacian pyramid; multi-focus
Authors: Madhu S Sigdel; Madhav Sigdel; Semih Dinç; Imren Dinç; Marc L Pusey; Ramazan S Aygün Journal: IEEE/ACM Trans Comput Biol Bioinform Date: 2016 Mar-Apr Impact factor: 3.710
Authors: Zev Rosenwaks; Olivier Elemento; Nikica Zaninovic; Iman Hajirasouliha; Pegah Khosravi; Ehsan Kazemi; Qiansheng Zhan; Jonas E Malmsten; Marco Toschi; Pantelis Zisimopoulos; Alexandros Sigaras; Stuart Lavery; Lee A D Cooper; Cristina Hickman; Marcos Meseguer Journal: NPJ Digit Med Date: 2019-04-04