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Oogenesis

Oogenesis

In human women, oogenesis produces one mature egg cell or ovum for every precursor cell that enters meiosis. This process differs in two unique ways from the equivalent procedure of spermatogenesis in males. First, meiotic divisions during oogenesis are asymmetric, meaning that a large oocyte (containing most of the cytoplasm) and minor polar body are produced as a result of meiosis I, and again following meiosis II. Since only oocytes will go on to form embryos if fertilized, this unequal...

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Related Experiment Video

Updated: Sep 8, 2025

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

Machine Learning Methods to Characterize Mouse and Human Oocytes.

Gaëlle Letort¹

¹Department of Developmental and Stem Cell Biology, Institut Pasteur, Université de Paris Cité, CNRS UMR 3738, Paris, France. gaelle.letort@pasteur.fr.

Methods in Molecular Biology (Clifton, N.J.)

|July 30, 2025

Summary

This summary is machine-generated.

This study introduces a machine learning pipeline for noninvasively phenotyping oocytes using transmitted light imaging. The system extracts morphological and dynamical features to characterize oocytes via machine learning algorithms.

Keywords:

Features Machine learning Morphology Oocyte Segmentation

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Last Updated: Sep 8, 2025

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Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse

Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse

Published on: September 3, 2021

Area of Science:

Reproductive Biology
Computational Biology
Biomedical Imaging

Background:

Oocyte quality assessment is crucial for reproductive success.
Current phenotyping methods can be invasive or lack detailed feature extraction.
Automated, noninvasive approaches are needed for efficient oocyte characterization.

Purpose of the Study:

To develop and validate a machine learning pipeline for noninvasive oocyte phenotyping.
To extract comprehensive morphological and dynamical features from oocyte images/movies.
To characterize oocytes using feature-based machine learning algorithms.

Main Methods:

Acquisition of oocyte images/movies using noninvasive transmitted light microscopy.
Image segmentation to delineate oocyte contours.
Extraction of hundreds of morphological and dynamical features.
Application of feature-based machine learning algorithms for oocyte characterization.

Main Results:

Successful implementation of a machine learning pipeline for oocyte phenotyping.
Extraction of a wide range of quantitative features describing oocyte morphology and dynamics.
Demonstration of feature-based machine learning for oocyte characterization.

Conclusions:

The developed pipeline offers a noninvasive, data-driven method for oocyte phenotyping.
This approach enables detailed characterization of individual oocytes based on extracted features.
Machine learning facilitates objective and efficient oocyte quality assessment.