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

The number and distribution of follicles in each growth stage provide a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. Due to the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of machine-learning algorithms has allowed for the development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more 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 three-dimensional microscopy images of whole mouse ovaries using a deep-learning convolutional neural network-based approach. We developed a fast tissue-clearing and imaging protocol to obtain three-dimensional images of whole-mount mouse ovaries. Fluorescently labeled oocytes from three-dimensional images were manually annotated in Napari to develop a training dataset. This dataset was used to retrain StarDist using a convolutional neural network within DL4MicEverywhere to automatically label all oocytes in the ovary. In a second phase, we utilize Accelerated Pixel and Object Classification, a Napari plugin, to sort oocytes into growth stages. Here, we provide an end-to-end pipeline for producing high-quality three-dimensional images of mouse ovaries and obtaining follicle counts and staging. We demonstrate how to customize OoCount to fit images produced in any lab. Using OoCount, we obtain oocyte counts from each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility.