Skip to Main content Skip to Navigation
Journal articles

High-throughput ovarian follicle counting by an innovative deep learning approach

Abstract : The evaluation of the number of mouse ovarian primordial follicles (PMF) can provide important information about ovarian function, regulation of folliculogenesis or the impact of chemotherapy on fertility. This counting, usually performed by specialized operators, is a tedious, time-consuming but indispensable procedure. The development and increasing use of deep machine learning algorithms promise to speed up and improve this process. Here, we present a new methodology of automatically detecting and counting PMF, using convolutional neural networks driven by labelled datasets and a sliding window algorithm to select test data. Trained from a database of 9 millions of images extracted from mouse ovaries, and tested over two ovaries (3 millions of images to classify and 2 000 follicles to detect), the algorithm processes the digitized histological slides of a completed ovary in less than one minute, dividing the usual processing time by a factor of about 30. It also outperforms the measurements made by a pathologist through optical detection. Its ability to correct label errors enables conducting an active learning process with the operator, improving the overall counting iteratively. These results could be suitable to adapt the methodology to the human ovarian follicles by transfer learning.
Keywords : CLASSIFICATION NUMBER
Document type :
Journal articles
Complete list of metadata

https://hal-cnrs.archives-ouvertes.fr/hal-03105451
Contributor : Colette Orange <>
Submitted on : Monday, January 11, 2021 - 10:14:32 AM
Last modification on : Wednesday, January 13, 2021 - 3:38:13 AM

Links full text

Identifiers

Citation

C Sonigo, S Jankowski, O Yoo, O Trassard, N Bousquet, et al.. High-throughput ovarian follicle counting by an innovative deep learning approach. Scientific Reports, Nature Publishing Group, 2018, 8, ⟨10.1038/s41598-018-31883-8⟩. ⟨hal-03105451⟩

Share

Metrics

Record views

10