An interpretable and versatile machine learning approach for oocyte phenotyping.

ABSTRACT: Meiotic maturation is a crucial step of oocyte formation, allowing its potential fertilization and embryo development. Elucidating this process is important for both fundamental research and assisted reproductive technology. However, few computational tools based on non-invasive measurements are available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images acquired in transmitted light. We trained neural networks to segment the contour of oocytes and their zona pellucida using oocytes from diverse species. We defined a comprehensive set of morphological features to describe an oocyte. These steps were implemented in an open-source Fiji plugin. We present a feature-based machine learning pipeline to recognize oocyte popu

SUBMITTER: Letort G 

PROVIDER: S-EPMC9377708 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature