Welcome to 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.

Here you will find a protocol designed to walk new users through the process from staining to data analysis as well as downloadable files to run the workflow. We have endeavored to make this as user friendly as possible and would appreciate your input on how to make this more accessible.

What you’ll Find Here:

1. Links to all relevant OoCount files including the StarDist models and example data sets.

2. A link to our Dryad repository for this work which has a detailed README file and complete OoCount Deliverables.

3. A link to “OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification” by Folts and collaborators. Which details every step of the process from staining and clearing ovaries to the complete Oocount pipeline

4. At the bottom of the page you will find a submission form. We encourage you to submit your data to us to make OoCount more versatile. It can additionally be used to submit questions and for help with troubleshooting.

OoCount Files

OoCount-StarDist Models

• For perinatal mouse ovaries: StardistOoCount_Perinatal-V1.4

• For adult ovaries: Stardist-OoCount_Adult-V1.2

Example Data (coming soon!)

• Perinatal ovary (postnatal day 3)

  • DDX4 image

  • NR5A2 image

  • StarDist labels

  • APOC classes

• Adult ovary (3 months)

  • DDX4 image

  • NR5A2 image

  • StarDist labels

  • APOC classes

Dryad Repository

Detailed OoCount Paper