High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies.

ABSTRACT: To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to

SUBMITTER: Asmar AJ 

PROVIDER: S-EPMC10878516 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature