An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

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Cheles, Doris Spinosa ; Ferreira, Andre Satoshi ; de Jesus, Isabela Sueitt ; Fernandez, Eleonora Inacio ; Pinheiro, Gabriel Martins ; Dal Molin, Eloiza Adriane ; Alves, Wallace ; Milanezi de Souza, Rebeca Colauto ; Bori, Lorena ; Meseguer, Marcos ; Rocha, Jose Celso ; Gouveia Nogueira, Marcelo Fabio

• Document type: Journal article
• Source: APPLIED SCIENCES-BASEL; v. 12, n. 7, p. 20-pg., 2022-04-01.

Abstract

Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope(R) and Geri(R), which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist's classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst's regions, with potential utility in embryo selection for assisted reproductive technology (ART). (AU)

• FAPESP's process: 20/07634-9 - Prediction of fetal heartbeat through artificial intelligence and morphological, morphokinetic and patient-related variables
• Grantee: Dóris Spinosa Chéles
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 18/24252-2 - Morphokinetics associated with artificial intelligence for the prediction of human blastocyst
• Grantee: Eleonora Inácio Fernandez
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 19/26749-4 - Application of artificial intelligence, deep learning and MLP techniques, to predict live birth in assisted reproduction patients using blastocyst morphology and patient physiological data
• Grantee: André Satoshi Ferreira
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 17/19323-5 - Classification of human embryos using the techniques of time-lapse, digital image processing and Artificial Intelligence.
• Grantee: José Celso Rocha
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/26684-0 - Use of artificial intelligence in predicting gestational success based on the selection of physiological variables of patients undergoing assisted reproduction
• Grantee: Eloiza Adriane Dal Molin
• Support Opportunities: Scholarships in Brazil - Scientific Initiation