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EmbSAM: cell boundary localization and Segment Anything Model for fast images of developing embryos.

Guoye Guan1,2, Cunmin Zhao3, Zelin Li4,5

  • 1Department of Systems Biology, Harvard Medical School, Boston, USA. guanguoye@gmail.com.

Communications Biology
|December 24, 2025
PubMed
Summary

We developed EmbSAM, a computational framework using deep learning, to accurately reconstruct cell shapes from low-quality live-imaging data. This enables detailed analysis of cell dynamics during early embryonic development.

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Area of Science:

  • Developmental biology
  • Cell biology
  • Computational imaging

Background:

  • Cellular shape dynamics are crucial for development but challenging to image due to low signal-to-noise ratios in live-cell microscopy.
  • Phototoxicity and photobleaching limit high-resolution, long-duration imaging of cell membranes, hindering accurate shape reconstruction.

Purpose of the Study:

  • To develop an integrative computational framework, EmbSAM, for accurate 3D cell membrane segmentation from challenging live-cell images.
  • To enable quantitative analysis of cell shape dynamics and morphodynamics during early embryonic development.

Main Methods:

  • Integration of a deep-learning-based cell boundary localization algorithm with the Segment Anything Model.
  • Application to 3D live-cell imaging of Caenorhabditis elegans embryos with high temporal resolution (10 seconds per stack).

Main Results:

  • EmbSAM achieved accurate segmentation of cell membranes despite low signal-to-noise ratios.
  • Quantitative characterization of cell-division-coupled morphodynamics, including cell position, division timing, and axis reorientation, prior to gastrulation.

Conclusions:

  • EmbSAM provides a robust method for analyzing cell shape dynamics in challenging imaging conditions.
  • The framework facilitates detailed investigation of morphogenetic processes and cell fate determination during embryogenesis.