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M. Villota^1,2, J. Ayensa-Jiménez^1,2, M. Doblaré^1,2 and J. Heras³

¹Aragón Health Research Institute (IIS Aragón) , Aragón, Spain
²Aragón Institute of Engineering Research (I3A) University of Zaragoza, Aragón, Spain
³Department of Mathematics and Computer Science University of La Rioja, La Rioja, Spain

doi:10.4203/ccc.6.9.7

M. Villota, J. Ayensa-Jiménez, M. Doblaré, J. Heras, "Segmentation of the blastocyst structures using Image Processing and Machine Learning tools", in P. Ivanyi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Seventeenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 6, Paper 9.7, 2023, doi:10.4203/ccc.6.9.7

Keywords: in vitro fertilization, preimplantation genetic screening, blastocyst grading, embryo quality assessment, image processing techniques, semantic segmentation.

Abstract

Embryo selection is a fundamental and indispensable step to ensure the success of in vitro fertilization. There are two techniques to perform embryo selection: preimplantation genetic screening and embryo morphological grading. However, even with these techniques, the embryo implantation probability is barely 65% making extremely difficult to evaluate the implantation potential. This is mainly due to the lack of markers, and the subjectivity associated with experience, judgement, and training of the embryologists. In contrast, a segmentation of the embryo structures offers detailed, quantitative, and objective assessments; and with that, information to predict the pregnancy outcome of embryos. In this work, two independent methods for embryos’ component segmentation are proposed. One is based on the combination of image processing techniques with genetic algorithms, and the other on a Deep Learning segmentation approach. Both methods allow us to approach state of the art results for embryos’ component segmentation.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 6 PROCEEDINGS OF THE SEVENTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: P. Ivanyi, J. Kruis and B.H.V. Topping Paper 9.7 Segmentation of the blastocyst structures using Image Processing and Machine Learning tools M. Villota^1,2, J. Ayensa-Jiménez^1,2, M. Doblaré^1,2 and J. Heras³ ¹Aragón Health Research Institute (IIS Aragón) , Aragón, Spain ²Aragón Institute of Engineering Research (I3A) University of Zaragoza, Aragón, Spain ³Department of Mathematics and Computer Science University of La Rioja, La Rioja, Spain doi:10.4203/ccc.6.9.7 Full Bibliographic Reference for this paper M. Villota, J. Ayensa-Jiménez, M. Doblaré, J. Heras, "Segmentation of the blastocyst structures using Image Processing and Machine Learning tools", in P. Ivanyi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Seventeenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 6, Paper 9.7, 2023, doi:10.4203/ccc.6.9.7 Keywords: in vitro fertilization, preimplantation genetic screening, blastocyst grading, embryo quality assessment, image processing techniques, semantic segmentation. Abstract Embryo selection is a fundamental and indispensable step to ensure the success of in vitro fertilization. There are two techniques to perform embryo selection: preimplantation genetic screening and embryo morphological grading. However, even with these techniques, the embryo implantation probability is barely 65% making extremely difficult to evaluate the implantation potential. This is mainly due to the lack of markers, and the subjectivity associated with experience, judgement, and training of the embryologists. In contrast, a segmentation of the embryo structures offers detailed, quantitative, and objective assessments; and with that, information to predict the pregnancy outcome of embryos. In this work, two independent methods for embryos’ component segmentation are proposed. One is based on the combination of image processing techniques with genetic algorithms, and the other on a Deep Learning segmentation approach. Both methods allow us to approach state of the art results for embryos’ component segmentation. download the full-text of this paper (PDF, 10 pages, 449 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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