DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy
Abolfazl Zargari, Gerrald A. Lodewijk, Najmeh Mashhadi, Nathan Cook, Celine Neudorf, Kimiasadat Araghbidikashani, Robert Hays, Sayaka Kozuki, Stefany Rubio, Eva Hrabeta‐Robinson, Angela N. Brooks, Lindsay Hinck, S. Ali Shariati
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 existing models. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells.