Automatic evaluation of human oocyte developmental potential from microscopy images

TL;DR

This paper presents an automated system that uses deep learning and machine learning techniques to evaluate human oocyte quality from microscopy images, aiming to assist embryologists in IVF procedures.

Contribution

It introduces a novel pipeline combining CNN-based segmentation and SVM classification for assessing oocyte developmental potential.

Findings

Achieved 70% classification accuracy.

Improved speed and consistency in oocyte evaluation.

Automated method aligns with expert annotations.

Abstract

Infertility is becoming an issue for an increasing number of couples. The most common solution, in vitro fertilization, requires embryologists to carefully examine light microscopy images of human oocytes to determine their developmental potential. We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first localize individual oocytes and identify their principal components using CNN (U-Net) segmentation. Next, we calculate several descriptors based on geometry and texture. The final step is an SVM classifier. Both the segmentation and classification training is based on expert annotations. The presented approach leads to a classification accuracy of 70%.