  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%.
