Microscopic Video-Based Grouped Embryo Segmentation: A Deep Learning Approach

Abstract

Purpose: The primary aim of this research is to enhance the utilization of advanced deep learning (DL) techniques in the domain of in vitro fertilization (IVF) by presenting a more refined approach to the segmentation and organization of microscopic embryos. This study also seeks to establish a comprehensive embryo database that can be employed for future research and educational purposes. Methods: This study introduces an advanced methodology for embryo segmentation and organization using DL. The approach comprises three primary steps: Embryo Segmentation Model, Segmented Embryo Image Organization, and Clear and Blur Image Classification. The proposed approach was rigorously evaluated on a sample of 5182 embryos extracted from 362 microscopic embryo videos. Results: The study’s results show that the proposed method is highly effective in accurately segmenting and organizing embryo images. This is evidenced by the high mean average precision values of 1.0 at an intersection over union threshold of 0.5 and across the range of 0.5 to 0.95, indicating a robust object detection capability that is vital in the IVF process. Segmentation of images based on various factors such as the day of development, patient, growth medium, and embryo facilitates easy comparison and identification of potential issues. Finally, appropriate threshold values for clear and blur image classification are proposed. Conclusion: The suggested technique represents an indispensable stage of data preparation for IVF training and education. Furthermore, this study provides a solid foundation for future research and adoption of DL in IVF, which is expected to have a significant positive impact on IVF outcomes.

Machine learning in time-lapse imaging to differentiate embryos from young vs old mice

“…A third type of newer machine learning that requires large computational power is deep learning, or artificial neural networks. Deep learning is useful to analyze very large datasets such as the pixels from embryo time-lapse images [ 50 , 51 ]. With recent advancements in computational power, numerous studies now employ artificial intelligence (AI) and deep learning technologies [ 50 , 52–54 ] to analyze still images [ 55–58 ] and videos [ 59 ] of embryo development.…”