Image Processing-Based Automation of Blastocoel Expansion Grading for Improved IVF Embryo Evaluation

Abstract

To increase implantation success rates, in vitro fertilization (IVF) depends on precise evaluation of blastocyst quality, with blastocoel growth, one of the blastocyst's characteristics. Blastocoel expansion being a critical evaluation parameter in IVF. In order to objectively measure blastocoel expansion from human blastocyst, this work presents an automated image analysis method. Preprocessing the image to improve quality, segmenting the blastocyst and blastocoel sections using image processing techniques, estimating the area of each region, and computing the ratio of blastocoel to blastocyst area are all phases in the proposed method. Expansion grades are assigned using these quantitative measurements, which aid in the selection of embryos during IVF operations. According to experimental data, the results offer objective, and consistent evaluation standards while lowering the subjective variability present in manual assessments. The automation of blastocoel assessment by the system provides reliable information to embryologists, enabling them to make more informed clinical decisions and perhaps increasing the success rate of embryo implantation during IVF treatments.