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Cleavage and Blastulation

Cleavage and Blastulation

After a large-single-celled zygote is produced via fertilization, the process of cleavage occurs while zygotes travel through the uterine tube. Cleavage is a mitotic cell division that does not result in growth. With each round of successive cell division, daughter cells get increasingly smaller.

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Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

Quantitative Analysis of Protein Expression to Study Lineage Specification in Mouse Preimplantation Embryos

Published on: February 22, 2016

Morphometric evaluation of two-pronucleus zygote images using image-processing techniques.

Niloofar Sayadi¹, Sara Monji-Azad², Seyed Abolghasem Mirroshandel¹

¹Department of Computer Engineering, University of Guilan, Rasht, Iran.

Zygote (Cambridge, England)

|August 17, 2022

Summary

An automated zygote morphology evaluation (ZME) algorithm analyzes human zygote images to identify viable embryos for in vitro fertilization (IVF). This fast, accurate method aids embryologists in selecting embryos with high implantation potential, reducing multiple pregnancies.

Keywords:

Image processing In vitro fertilization Morphometric evaluation Pronucleus morphology Zygote

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Area of Science:

Reproductive biology and assisted reproductive technologies.

Background:

Selecting viable embryos for implantation is a significant challenge in in vitro fertilization (IVF).
High rates of multiple pregnancies in IVF cycles lead to adverse economic and health outcomes.
Transferring a single embryo with high implantation potential is the optimal strategy.

Purpose of the Study:

To develop an automated algorithm for zygote morphometric evaluation to improve embryo selection in IVF.
To analyze key morphological indicators of embryo viability and normality.

Main Methods:

An automated zygote morphology evaluation (ZME) algorithm was developed, incorporating a noise reduction step.
The algorithm identifies and measures critical zygote structures: oolemma, perivitelline space, zona pellucida, and nucleolar precursor bodies (NPBs).
A novel dataset of 703 human zygote images (HZME-DS) was utilized for algorithm training and validation.

Main Results:

The ZME algorithm achieved high accuracy in identifying zygote regions: 79.58% for oolemma, 79.40% for perivitelline space, and 79.72% for zona pellucida.
For nucleolar precursor bodies (NPBs), the F1 score reached 81.14% (Recall) and 79.53% (Precision).
The algorithm demonstrated a fast and automated approach to zygote analysis.

Conclusions:

The ZME algorithm provides an accurate and efficient method for evaluating zygote morphology.
This automated tool can assist embryologists in real-time selection of high-potential embryos during IVF procedures.
The ZME algorithm has the potential to enhance IVF success rates and reduce complications associated with multiple pregnancies.