A Machine Learning-Based Image Segmentation Method to Quantify In Vitro Osteoclast Culture Endpoints.

ABSTRACT: Quantification of in vitro osteoclast cultures (e.g. cell number) often relies on manual counting methods. These approaches are labour intensive, time consuming and result in substantial inter- and intra-user variability. This study aimed to develop and validate an automated workflow to robustly quantify in vitro osteoclast cultures. Using ilastik, a machine learning-based image analysis software, images of tartrate resistant acid phosphatase-stained mouse osteoclasts cultured on dentine discs were used to train the ilastik-based algorithm. Assessment of algorithm training showed that osteoclast numbers strongly correlated between manual- and automatically quantified values (r = 0.87). Osteoclasts were consistently faithfully segmented by the model when visually compared to the original re

SUBMITTER: Davies BK 

PROVIDER: S-EPMC10516805 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature