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Open AccessArticle

HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks

1
Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
2
Department of Control Systems, Kaunas University of Technology, 51367 Kaunas, Lithuania
3
Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
4
Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
5
Institute of Computational Intelligence, Czestochowa University of Technology, 42-200 Czestochowa, Poland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3578; https://doi.org/10.3390/s19163578
Received: 26 July 2019 / Revised: 13 August 2019 / Accepted: 14 August 2019 / Published: 16 August 2019
We propose a method for generating the synthetic images of human embryo cells that could later be used for classification, analysis, and training, thus resulting in the creation of new synthetic image datasets for research areas lacking real-world data. Our focus was not only to generate the generic image of a cell such, but to make sure that it has all necessary attributes of a real cell image to provide a fully realistic synthetic version. We use human embryo images obtained during cell development processes for training a deep neural network (DNN). The proposed algorithm used generative adversarial network (GAN) to generate one-, two-, and four-cell stage images. We achieved a misclassification rate of 12.3% for the generated images, while the expert evaluation showed the true recognition rate (TRR) of 80.00% (for four-cell images), 86.8% (for two-cell images), and 96.2% (for one-cell images). Texture-based comparison using the Haralick features showed that there is no statistically (using the Student’s t-test) significant (p < 0.01) differences between the real and synthetic embryo images except for the sum of variance (for one-cell and four-cell images), and variance and sum of average (for two-cell images) features. The obtained synthetic images can be later adapted to facilitate the development, training, and evaluation of new algorithms for embryo image processing tasks. View Full-Text
Keywords: deep learning; neural network; generative adversarial network; synthetic images deep learning; neural network; generative adversarial network; synthetic images
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MDPI and ACS Style

Dirvanauskas, D.; Maskeliūnas, R.; Raudonis, V.; Damaševičius, R.; Scherer, R. HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks. Sensors 2019, 19, 3578.

AMA Style

Dirvanauskas D, Maskeliūnas R, Raudonis V, Damaševičius R, Scherer R. HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks. Sensors. 2019; 19(16):3578.

Chicago/Turabian Style

Dirvanauskas, Darius; Maskeliūnas, Rytis; Raudonis, Vidas; Damaševičius, Robertas; Scherer, Rafal. 2019. "HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks" Sensors 19, no. 16: 3578.

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