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

Cell Rep Methods. 2023 Jun 12;3(6):100500. doi: 10.1016/j.crmeth.2023.100500. eCollection 2023 Jun 26.

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.

Keywords: Cell biology; cell segmentation; cell size; cell tracking; deep learning; live imaging; microscopy.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Microscopy*
  • Microscopy, Phase-Contrast
  • Time-Lapse Imaging / methods