Proliferating cell nuclear antigen (PCNA) allows the automatic identification of follicles in microscopic images of human ovarian tissue

TL;DR

This study introduces an automated method using PCNA staining and image analysis to accurately identify and count follicles in human ovarian tissue, enabling large-scale ovarian reserve assessment.

Contribution

The paper presents a novel automated approach combining PCNA immunohistochemistry and computational analysis for follicle identification in human ovarian tissue, improving accuracy and scalability.

Findings

Automated estimates within 10% of human experts' gold standard.

Method effective at 100x and 200x magnifications.

Demonstrates viability for large-scale ovarian reserve studies.

Abstract

Human ovarian reserve is defined by the population of nongrowing follicles (NGFs) in the ovary. Direct estimation of ovarian reserve involves the identification of NGFs in prepared ovarian tissue. Previous studies involving human tissue have used hematoxylin and eosin (HE) stain, with NGF populations estimated by human examination either of tissue under a microscope, or of images taken of this tissue. In this study we replaced HE with proliferating cell nuclear antigen (PCNA), and automated the identification and enumeration of NGFs that appear in the resulting microscopic images. We compared the automated estimates to those obtained by human experts, with the "gold standard" taken to be the average of the conservative and liberal estimates by three human experts. The automated estimates were within 10% of the "gold standard", for images at both 100x and 200x magnifications. Automated analysis took longer than human analysis for several hundred images, not allowing for breaks from analysis needed by humans. Our results both replicate and improve on those of previous studies involving rodent ovaries, and demonstrate the viability of large-scale studies of human ovarian reserve using a combination of immunohistochemistry and computational image analysis techniques.