{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T05:04:51Z","timestamp":1774328691137,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T00:00:00Z","timestamp":1565913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program of the Polish Minister of Science and Higher Education under the name &quot;Regional Initiative of Excellence&quot;","award":["020\/RID\/2018\/19"],"award-info":[{"award-number":["020\/RID\/2018\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s t-test) significant (p &lt; 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.<\/jats:p>","DOI":"10.3390\/s19163578","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"3578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5471-1893","authenticated-orcid":false,"given":"Darius","family":"Dirvanauskas","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"}]},{"given":"Vidas","family":"Raudonis","sequence":"additional","affiliation":[{"name":"Department of Control Systems, Kaunas University of Technology, 51367 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"},{"name":"Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-262X","authenticated-orcid":false,"given":"Rafal","family":"Scherer","sequence":"additional","affiliation":[{"name":"Institute of Computational Intelligence, Czestochowa University of Technology, 42-200 Czestochowa, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_3","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014, January 21\u201326). DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. Proceedings of the 31st International Conference on Machine Learning, Beijing, China."},{"key":"ref_4","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations ICLR, San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jersey, NJ, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR \u201914), Washington, DC, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_9","unstructured":"Fakoor, R., Ladhak, F., Nazi, A., and Huber, M. (2013). Using deep learning to enhance cancer diagnosis and classification. ICML Workshop on the Role of Machine Learning in Transforming Healthcare Proceedings of the International Conference on Machine Learning, Atlanta, AG, USA, 16\u201321 June 2013, ACM."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ciresan, D.C., Giusti, A., Gambardella, L.M., and Schmidhuber, J. (2013, January 22\u201326). Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, Nagoya, Japan.","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"ref_11","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Proceedings of the Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dong, B., Shao, L., Da Costa, M., Bandmann, O., and Frangi, A.F. (2015, January 16\u201319). Deep learning for automatic cell detection in wide-field microscopy zebrafish images. Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA.","DOI":"10.1109\/ISBI.2015.7163986"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kheradmand, S., Singh, A., Saeedi, P., Au, J., and Havelock, J. (2017, January 17\u201320). Inner cell mass segmentation in human HMC embryo images using fully convolutional network. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296582"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rbmo.2012.09.015","article-title":"Artificial intelligence techniques for embryo and oocyte classification","volume":"26","author":"Manna","year":"2013","journal-title":"Reprod. Biomed. Online"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Y., Moussavi, F., and Lorenzen, P. (2013, January 22\u201326). Automated Embryo Stage Classification in Time-Lapse Microscopy Video of Early Human Embryo Development. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI (2013), Nagoya, Japan.","DOI":"10.1007\/978-3-642-40763-5_57"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khan, A., Gould, S., and Salzmann, M. (2015, January 16\u201319). Automated monitoring of human embryonic cells up to the 5-cell stage in time-lapse microscopy images. Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, USA.","DOI":"10.1109\/ISBI.2015.7163894"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khan, A., Gould, S., and Salzmann, M. (2015, January 6\u20139). A Linear Chain Markov Model for Detection and Localization of Cells in Early Stage Embryo Development. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.76"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/978-1-60327-292-6_16","article-title":"Segmentation and Quantitative Analysis of Individual Cells in Developmental Tissues","volume":"Volume 1092","author":"Lewandoski","year":"2014","journal-title":"Mouse Molecular Embryology; Methods in Molecular Biology (Methods and Protocols)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1002\/cyto.a.20162","article-title":"Whole cell segmentation in solid tissue sections","volume":"67A","author":"Baggett","year":"2005","journal-title":"Cytometry"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.cmpb.2019.05.027","article-title":"Embryo development stage prediction algorithm for automated time lapse incubators","volume":"177","author":"Dirvanauskas","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Osokin, A., Chessel, A., Salas, R.E.C., and Vaggi, F. (2017, January 22\u201329). GANs for Biological Image Synthesis. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.245"},{"key":"ref_22","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of GANs for improved quality, stability, and variation. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, C., Rundo, L., Araki, R., Furukawa, Y., Mauri, G., Nakayama, H., and Hayashi, H. (2019). Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection. arXiv.","DOI":"10.1007\/978-981-13-8950-4_27"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Hayat, M., and Barnes, N. (2018, January 12\u201315). Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA.","DOI":"10.1109\/WACV.2018.00148"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yi, X., Walia, E., and Babyn, P. (2018). Generative adversarial network in medical imaging: A review. arXiv.","DOI":"10.1016\/j.media.2019.101552"},{"key":"ref_26","unstructured":"Bengio, Y., Thibodeau-Laufer, \u00c9., Alain, G., and Yosinski, J. (2014, January 21\u201326). Deep Generative Stochastic Networks Trainable by Backprop. Proceedings of the 31st International Conference on International Conference on Machine Learning, Beijing, China."},{"key":"ref_27","unstructured":"Gregor, K., Danihelka, I., Graves, A., Jimenez Rezende, D., and Wierstra, D. (2015, January 6\u201311). DRAW: A Recurrent Neural Network for Image Generation. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Springenberg, J.T., and Brox, T. (2015, January 7\u201312). Learning to generate chairs with convolutional neural networks. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298761"},{"key":"ref_29","first-page":"1486","article-title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","volume":"Volume 1","author":"Denton","year":"2015","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems-Volume 1 (NIPS\u201915)"},{"key":"ref_30","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_31","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H. (2016, January 20\u201322). Generative Adversarial Text to Image Synthesis. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. (2017, January 22\u201329). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Liu, M.-Y., Yang, X., and Kautz, J. (2018, January 18\u201322). MoCoGAN: Decomposing Motion and Content for Video Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake, UT, USA.","DOI":"10.1109\/CVPR.2018.00165"},{"key":"ref_34","unstructured":"Vondrick, C., Pirsiavash, H., and Torralba, A. (2017, January 4\u20139). Generating Videos with Scene Dynamics. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS\u201916), Long Beach, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Saito, M., Matsumoto, E., and Saito, S. (2017, January 22\u201329). Temporal Generative Adversarial Nets with Singular Value Clipping. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.308"},{"key":"ref_36","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, W.T., and Tenenbaum, J.B. (2016, January 15\u201319). Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS\u201916), Boston, MA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7350324","DOI":"10.1155\/2018\/7350324","article-title":"DR-Net: A Novel Generative Adversarial Network for Single Image Deraining","volume":"2018","author":"Li","year":"2018","journal-title":"Secur. Commun. Netw."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhu, D., Dai, L., Luo, Y., Zhang, G., Shao, X., Itti, L., and Lu, J. (2018). Multi-Scale Adversarial Feature Learning for Saliency Detection. Symmetry, 10.","DOI":"10.3390\/sym10100457"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, K., Guan, Z., Xu, X., Qian, X., and Bao, H. (2018). Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing. Symmetry, 10.","DOI":"10.3390\/sym10120734"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Han, C., Hayashi, H., Rundo, L., Araki, R., Shimoda, W., Muramatsu, S., and Nakayama, H. (2018, January 4\u20137). GAN-based synthetic brain MR image generation. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363678"},{"key":"ref_41","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2 (NIPS\u201914), Montreal, QC, Canada."},{"key":"ref_42","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). Rectifier nonlinearities improve neural network acoustic model. Proceedings of the International Conference on Machine ICML 2013, Atlanta, GA, USA."},{"key":"ref_43","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37 (ICML\u201915), Lille, France."},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2014, January 24\u201328). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, Indianapolis, IN, USA."},{"key":"ref_45","unstructured":"Theis, L., van den Oord, A., and Bethge, M. (2015, January 19). A note on the evaluation of generative models. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_46","unstructured":"Im, D.J., Kim, C.D., Jiang, H., and Memisevic, R. (2016). Generating Images with Recurrent Adversarial Networks. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Geman, D., Geman, S., Hallonquist, N., and Younes, L. (2015, January 22\u201329). Visual Turing test for computer vision systems. Proceedings of the National Academy of Sciences, Washington, DC, USA.","DOI":"10.1073\/pnas.1422953112"},{"key":"ref_48","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5\u201310). Improved techniques for training GANs. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS\u201916), Barcelona, Spain."},{"key":"ref_49","unstructured":"Tazehkandi, A.A. (2018). Computer Vision with OpenCV 3 and Qt5, Packt Publishing."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_51","unstructured":"Kingma, D.P., Rezende, D.J., Mohamed, S., and Welling, M. (2014, January 8\u201313). Semi-supervised learning with deep generative models. Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2 (NIPS\u201914), Montreal, QC, Canada."},{"key":"ref_52","unstructured":"Maal\u00f8e, L., S\u00f8nderby, C.K., S\u00f8nderby, S.K., and Winther, O. (2016, January 19\u201324). Auxiliary deep generative models. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_53","unstructured":"Dumoulin, V., Belghazi, I., Poole, B., Lamb, M.A., Mastropietro, O., and Courville, A. (2017, January 24\u201326). Adversarially Learned Inference. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:45Z","timestamp":1760188305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,16]]},"references-count":53,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19163578"],"URL":"https:\/\/doi.org\/10.3390\/s19163578","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,16]]}}}