OoCount: A machine-learning based approach to mouse ovarian follicle counting and classification

The number and distribution of ovarian follicles in each growth stage provides a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional (3D) imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. However, because of the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of deep-learning algorithms has allowed for the rapid development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more user-friendly and accessible to non-specialists. We used these tools to create OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a deep-learning convolutional neural network (CNN) based approach. We developed a fast clearing and spinning disk confocal-based imaging protocol to obtain 3D images of whole mount perinatal and adult mouse ovaries. Then, fluorescently labeled oocytes from 3D images of ovaries were manually annotated to develop a machine learning training dataset. This dataset was used to train a CNN to automatically label all oocytes in the ovary. In a second phase, we trained another CNN to classify labeled oocytes and sort them into growth stages. Using OoCount, we can obtain accurate counts of oocytes in each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility. Here, we provide an end-to-end protocol for developing high quality 3D images of the perinatal and adult mouse ovary, obtaining follicle counts and stages, and how to customize OoCount to fit images produced in any lab.

Methods
Please see accompanying article: Folts et al. (2024) OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification.