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 whol..., Please see accompanying article: Folts et al. (2024) OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification. , , # Data from: OoCount: A machine-learning based approach to mouse ovarian follicle counting and classification

**Purpose of Oocount:**

OoCount is a high throughput open-source pipeline we have developed with the specific aim of using whole-mount immunofluorescence and 3D imaging techniques to label all oocytes in the mouse ovary. Using tools such as Napari,  DL4MicEverywhere, StarDist, and APOC, we assembled a machine learning-based workflow to automate oocyte counts and classification.

Our hope is that researchers in the field of ovarian biology utilize this tool as a more definitive and accurate methodology than traditional serial sectioning for evaluating ovarian homeostasis in an *in toto* context.

To get started the user will want to refer to \"OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification\", Folts et al., 2024 which contains the protocol for clearing, staining and imaging mouse ovaries. Once the images have been obtained this pip...