Follicle identification in primate ovaries via machine learning†

Studies of ovarian health and aging rely on estimates of the ovarian reserve, i.e., the number of healthy ovarian follicles. We present a machine learning pipeline to classify preantral ovarian follicles in histology images from non-human primates. Ovarian tissue from 11 monkeys were fixed, sectioned, stained, and imaged. A total of 7770 preantral follicles were manually annotated in 18 histology section images using a standardized protocol, with annotations reviewed by an expert. Using a transfer learning approach, we retrained a ResNet34 convolutional neural network to classify individual follicle images into six developmental stages. The model achieved high accuracy during training (>98%) and good performance on validation and test sets (75.3% and 72.4%, respectively), with most errors confined to adjacent stages, suggesting the network learned an implicit developmental ordering of follicles. All code, annotated datasets, and trained models are publicly available to support reuse and further exploration. Application of this model for determining follicle numbers will be useful in studies of primates, and perhaps humans. For example, it may be used for determining drug or environmental toxicant effects on follicle populations or for fertility preservation planning. Importantly, this model will facilitate the study of ovarian aging and follicle depletion in a translational animal model and for developing interventions to preserve ovarian reserve, extend fertility, and delay menopause in women.