A Review on Automatic Analysis of Human Embryo Microscope Images

Over the last 30 years the process of in vitro fertilisation (IVF) has evolved considerably, yet the efficiency of this treatment remains relatively poor. The principal challenge faced by doctors and embryologists is the identification of the embryo with the greatest potential for producing a child. Current methods of embryo viability assessment provide only a rough guide to potential. In order to improve the odds of a successful pregnancy it is typical to transfer more than one embryo to the uterus. However, this often results in multiple pregnancies (twins, triplets, etc), which are associated with significantly elevated risks of serious complications. If embryo viability could be assessed more accurately, it would be possible to transfer fewer embryos without negatively impacting IVF pregnancy rates. In order to assist with the identification of viable embryos, several scoring systems based on morphological criteria have been developed. However, these mostly rely on a subjective visual analysis. Automated assessment of morphological features offers the possibility of more accurate quantification of key embryo characteristics and elimination of inter- and intra-observer variation. In this paper, we describe the main embryo scoring systems currently in use and review related works on embryo image analysis that could lead to an automatic and precise grading of embryo quality. We summarise achievements, discuss challenges ahead, and point to some possible future directions in this research field

A minimally invasive methodology based on morphometric parameters for day 2 embryo quality assessment

[EN] The risk of multiple pregnancy to maternal fetal health can be minimized by reducing the number of embryos transferred. New tools for selecting embryos with the highest implantation potential should be developed. The aim of this study was to evaluate the ability of morphological and morphometric variables to predict implantation by analysing images of embryos. This was a retrospective study of 135 embryo photographs from 112 IVF ICSI cycles carried out between January and March 2011. The embryos were photographed immediately before transfer using Cronus 3 software. Their images were analysed using the public program ImageJ. Significant effects (P < 0.05), and higher discriminant power to predict implantation were observed for the morphometric embryo variables compared with morphological ones. The features for successfully implanted embryos were as follows: four cells on day 2 of development; all blastomeres with circular shape (roundness factor greater than 0.9), an average zona pellucida thickness of 13&#8201;µm and an average of 17695.1&#8201;µm2 for the embryo area. Embryo size, which is described by its area and the average roundness factor for each cell, provides two objective variables to consider when predicting implantation. This approach should be further investigated for its potential ability to improve embryo scoring.Molina Botella, MI.; Lázaro Ibáñez, E.; Pertusa, J.; Debón Aucejo, AM.; Martinez Sanchis, JV.; Pellicer Bofill, AJ. (2014). A minimally invasive methodology based on morphometric parameters for day 2 embryo quality assessment. Reproductive BioMedicine Online. 29(4):470-480. doi:10.1016/j.rbmo.2014.06.005S47048029

Methods for Spatio-Temporal Analysis of Embryo Cleavage In Vitro

Automated or semiautomated time-lapse analysis of early stage embryo images during the cleavage stage can give insight into the timing of mitosis, regularity of both division timing and pattern, as well as cell lineage. Simultaneous monitoring of molecular processes enables the study of connections between genetic expression and cell physiology and development. The study of live embryos poses not only new requirements on the hardware and embryo-holding equipment but also indirectly on analytical software and data analysis as four-dimensional video sequencing of embryos easily creates high quantities of data. The ability to continuously film and automatically analyze growing embryos gives new insights into temporal embryo development by studying morphokinetics as well as morphology. Until recently, this was not possible unless by a tedious manual process. In recent years, several methods have been developed that enable this dynamic monitoring of live embryos. Here we describe three methods with variations in hardware and software analysis and give examples of the outcomes. Together, these methods open a window to new information in developmental embryology, as embryo division pattern and lineage are studied in vivo

Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential

[ES] Objetivo: Describir nuevas características de embriones capaces de predecir el potencial de implantación como datos de entrada para un modelo de red neuronal artificial (ANN). Diseño: Estudio de cohorte retrospectivo. Entorno: Centro de FIV privado afiliado a la universidad. Paciente (s): Este estudio incluyó a 637 pacientes del programa de donación de ovocitos que se sometieron a transferencia de un solo blastocisto durante dos años consecutivos. Intervención (es): Ninguna. Principales medidas de resultado: La investigación se dividió en dos fases. La fase 1 consistió en la descripción y análisis de las siguientes características embrionarias en embriones implantados y no implantados: distancia y velocidad de migración pronuclear, diámetro del blastocisto expandido, área de masa celular interna y duración del ciclo celular del trofoectodermo. La fase 2 consistió en el desarrollo de un algoritmo ANN para la predicción de la implantación. Se obtuvieron resultados para cuatro modelos alimentados con diferentes datos de entrada. El poder predictivo se midió con el uso del área bajo la curva característica operativa del receptor (AUC). Resultado (s): De los cinco nuevos parámetros descritos, el diámetro expandido del blastocisto y la duración del ciclo celular del trofoectodermo tenían valores estadísticamente diferentes en los embriones implantados y no implantados. Después de que los modelos ANN fueron entrenados y validados mediante validación cruzada cinco veces, estos fueron capaces de predecir la implantación en los datos de prueba con AUC de 0,64 para ANN1 (morfocinética convencional), 0,73 para ANN2 (morfodinámica novedosa), 0,77 para ANN3 (morfocinética convencional þ morfodinámica novedosa) y 0,68 para ANN4 (variables discriminatorias de prueba estadística). Conclusión (es): Las nuevas características embrionarias propuestas afectan al potencial de implantación y su combinación con parámetros morfocinéticos convencionales es eficaz como datos de entrada para un modelo predictivo basado en inteligencia artificial.[EN] Objective: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model. Design: Retrospective cohort study. Setting: University-affiliated private IVF center. Patient(s): This study included 637 patients from the oocyte donation program who underwent single-blastocyst transfer during two consecutive years. Intervention(s): None. Main Outcome Measure(s): The research was divided into two phases. Phase 1 consisted of the description and analysis of the following embryo features in implanted and nonimplanted embryos: distance and speed of pronuclear migration, blastocyst expanded diameter, inner cell mass area, and trophectoderm cell cycle length. Phase 2 consisted of the development of an ANN algorithm for implantation prediction. Results were obtained for four models fed with different input data. The predictive power was measured with the use of the area under the receiver operating characteristic curve (AUC). Result(s): Out of the five novel described parameters, blastocyst expanded diameter and trophectoderm cell cycle length had statistically different values in implanted and nonimplanted embryos. After the ANN models were trained and validated using fivefold cross validation, they were capable of predicting implantation on testing data with AUCs of 0.64 for ANN1 (conventional morphokinetics), 0.73 for ANN2 (novel morphodynamics), 0.77 for ANN3 (conventional morphokinetics thorn novel morphodynamics), and 0.68 for ANN4 (discriminatory variables from statistical test). Conclusion(s): The novel proposed embryo features affect the implantation potential, and their combination with conventional morphokinetic parameters is effective as input data for a predictive model based on artificial intelligence. ((c) 2020 by American Society for Reproductive Medicine.)Supported by the Ministry of Science, Innovation, and Universities CDTI (IDI-20191102), an Industrial Ph.D. grant (DIN2018-009911), and Agencia Valenciana de Innovacio (INNCAD00-18-009) to E.P. and M.M.Bori, L.; Paya-Bosch, E.; Alegre, L.; Viloria, T.; Remohí, J.; Naranjo Ornedo, V.; Meseguer, M. (2020). Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertility and Sterility. 114(6):1232-1241. https://doi.org/10.1016/j.fertnstert.2020.08.023S123212411146De Geyter, C., Calhaz-Jorge, C., Kupka, M. S., Wyns, C., Mocanu, E., Motrenko, T., … Goossens, V. (2018). 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Reproductive Biology, 17(2), 144-150. doi:10.1016/j.repbio.2017.03.004Desch, L., Bruno, C., Luu, M., Barberet, J., Choux, C., Lamotte, M., … Fauque, P. (2017). Embryo multinucleation at the two-cell stage is an independent predictor of intracytoplasmic sperm injection outcomes. Fertility and Sterility, 107(1), 97-103.e4. doi:10.1016/j.fertnstert.2016.09.022Kirkegaard, K., Hindkjaer, J. J., Grøndahl, M. L., Kesmodel, U. S., & Ingerslev, H. J. (2012). A randomized clinical trial comparing embryo culture in a conventional incubator with a time-lapse incubator. Journal of Assisted Reproduction and Genetics, 29(6), 565-572. doi:10.1007/s10815-012-9750-xWong, C. C., Loewke, K. E., Bossert, N. L., Behr, B., De Jonge, C. J., Baer, T. M., & Pera, R. A. R. (2010). Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nature Biotechnology, 28(10), 1115-1121. doi:10.1038/nbt.1686Conaghan, J., Chen, A. A., Willman, S. P., Ivani, K., Chenette, P. E., Boostanfar, R., … Shen, S. (2013). Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial. Fertility and Sterility, 100(2), 412-419.e5. doi:10.1016/j.fertnstert.2013.04.021Milewski, R., Kuć, P., Kuczyńska, A., Stankiewicz, B., Łukaszuk, K., & Kuczyński, W. (2015). A predictive model for blastocyst formation based on morphokinetic parameters in time-lapse monitoring of embryo development. Journal of Assisted Reproduction and Genetics, 32(4), 571-579. doi:10.1007/s10815-015-0440-3Chamayou, S., Patrizio, P., Storaci, G., Tomaselli, V., Alecci, C., Ragolia, C., … Guglielmino, A. (2013). The use of morphokinetic parameters to select all embryos with full capacity to implant. Journal of Assisted Reproduction and Genetics, 30(5), 703-710. doi:10.1007/s10815-013-9992-2Milewski, R., Czerniecki, J., Kuczyńska, A., Stankiewicz, B., & Kuczyński, W. (2016). Morphokinetic parameters as a source of information concerning embryo developmental and implantation potential. Ginekologia Polska, 87(10), 677-684. doi:10.5603/gp.2016.0067Motato, Y., de los Santos, M. J., Escriba, M. J., Ruiz, B. A., Remohí, J., & Meseguer, M. (2016). Morphokinetic analysis and embryonic prediction for blastocyst formation through an integrated time-lapse system. Fertility and Sterility, 105(2), 376-384.e9. doi:10.1016/j.fertnstert.2015.11.001Petersen, B. M., Boel, M., Montag, M., & Gardner, D. K. (2016). Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3. Human Reproduction, 31(10), 2231-2244. doi:10.1093/humrep/dew188Meseguer, M., Herrero, J., Tejera, A., Hilligsoe, K. M., Ramsing, N. B., & Remohi, J. (2011). The use of morphokinetics as a predictor of embryo implantation. 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Human Reproduction, 30(2), 276-283. doi:10.1093/humrep/deu331Aparicio-Ruiz, B., Romany, L., & Meseguer, M. (2018). Selection of preimplantation embryos using time-lapse microscopy in in vitro fertilization: State of the technology and future directions. Birth Defects Research, 110(8), 648-653. doi:10.1002/bdr2.1226Barrie, A., Homburg, R., McDowell, G., Brown, J., Kingsland, C., & Troup, S. (2017). Preliminary investigation of the prevalence and implantation potential of abnormal embryonic phenotypes assessed using time-lapse imaging. Reproductive BioMedicine Online, 34(5), 455-462. doi:10.1016/j.rbmo.2017.02.011Campbell, A., Fishel, S., Bowman, N., Duffy, S., Sedler, M., & Hickman, C. F. L. (2013). Modelling a risk classification of aneuploidy in human embryos using non-invasive morphokinetics. Reproductive BioMedicine Online, 26(5), 477-485. doi:10.1016/j.rbmo.2013.02.006Desai, N., Goldberg, J. M., Austin, C., & Falcone, T. (2018). 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Examining the efficacy of six published time-lapse imaging embryo selection algorithms to predict implantation to demonstrate the need for the development of specific, in-house morphokinetic selection algorithms. Fertility and Sterility, 107(3), 613-621. doi:10.1016/j.fertnstert.2016.11.014Coticchio, G., Mignini Renzini, M., Novara, P. V., Lain, M., De Ponti, E., Turchi, D., … Dal Canto, M. (2017). Focused time-lapse analysis reveals novel aspects of human fertilization and suggests new parameters of embryo viability. Human Reproduction, 33(1), 23-31. doi:10.1093/humrep/dex344Aguilar, J., Motato, Y., Escribá, M. J., Ojeda, M., Muñoz, E., & Meseguer, M. (2014). The human first cell cycle: impact on implantation. Reproductive BioMedicine Online, 28(4), 475-484. doi:10.1016/j.rbmo.2013.11.014Barberet, J., Bruno, C., Valot, E., Antunes-Nunes, C., Jonval, L., Chammas, J., … Fauque, P. (2019). Can novel early non-invasive biomarkers of embryo quality be identified with time-lapse imaging to predict live birth? Human Reproduction, 34(8), 1439-1449. doi:10.1093/humrep/dez085Richter, K. S., Harris, D. C., Daneshmand, S. T., & Shapiro, B. S. (2001). Quantitative grading of a human blastocyst: optimal inner cell mass size and shape. Fertility and Sterility, 76(6), 1157-1167. doi:10.1016/s0015-0282(01)02870-9Shapiro, B. S., Daneshmand, S. T., Garner, F. C., Aguirre, M., & Thomas, S. (2008). Large blastocyst diameter, early blastulation, and low preovulatory serum progesterone are dominant predictors of clinical pregnancy in fresh autologous cycles. Fertility and Sterility, 90(2), 302-309. doi:10.1016/j.fertnstert.2007.06.062Almagor, M., Harir, Y., Fieldust, S., Or, Y., & Shoham, Z. (2016). Ratio between inner cell mass diameter and blastocyst diameter is correlated with successful pregnancy outcomes of single blastocyst transfers. 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Automating assessment of human embryo images and time-lapse sequences for IVF treatment

As the number of couples using In Vitro Fertilization (IVF) treatment to give birth increases, so too does the need for robust tools to assist embryologists in selecting the highest quality embryos for implantation. Quality scores assigned to embryonic structures are critical markers for predicting implantation potential of human blastocyst-stage embryos. Timing at which embryos reach certain cell and development stages in vitro also provides valuable information about their development progress and potential to become a positive pregnancy. The current workflow of grading blastocysts by visual assessment is susceptible to subjectivity between embryologists. Visually verifying when embryo cell stage increases is tedious and confirming onset of later development stages is also prone to subjective assessment. This thesis proposes methods to automate embryo image and time-lapse sequence assessment to provide objective evaluation of blastocyst structure quality, cell counting, and timing of development stages