# Automatic evaluation of human oocyte developmental potential from microscopy images

arXiv: https://arxiv.org/abs/2103.00302 · HTML: https://mlsanity.com/paper/2103.00302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Grading of Mammalian Cumulus Oocyte Complexes using Machine Learning for in Vitro Embryo Culture](https://mlsanity.com/paper/1603.01739/) (2016)

## Built on by (most cited first, 1 of 1)

- [Fast learning from label proportions with small bags](https://mlsanity.com/paper/2110.03426/) (2021)

## Abstract (arXiv)

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%.