DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.

ABSTRACT: Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. However, segmentation and tracking of single cells remain challenging for the analysis of time-lapse microscopy images, in particular for widely available and non-toxic imaging modalities such as phase-contrast imaging. This work presents a versatile and trainable deep-learning model, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images with higher precision than exi

SUBMITTER: Zargari A 

PROVIDER: S-EPMC10326378 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature