Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture

TL;DR

This paper introduces a semi-automatic machine learning framework that uses image analysis and random forest classification to assess mammalian oocyte quality, aiming to improve objectivity and efficiency over traditional visual and molecular methods.

Contribution

It proposes a novel semi-automatic approach combining multi-object segmentation and random forest classification for oocyte grading, reducing subjectivity and manual effort.

Findings

Effective classification of oocyte quality achieved

Reduces inter-observer variability

Provides a standardized assessment method

Abstract

Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly. Image analysis of mammalian oocytes can provide attractive alternative to address this challenge. However, it is complex, given the huge number of oocytes under inspection and the subjective nature of the features inspected for identification. Supervised machine learning methods like random forest with annotations from expert biologists can make the analysis task standardized and reduces inter-subject variability. We present a semi-automatic framework for predicting the class an oocyte belongs to, based on multi-object parametric segmentation on the acquired microscopic image followed by a feature based classification using random forests.