Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D

John W. Wills, Jack Robertson, Pani Tourlomousis, Clare M.C. Gillis, Claire M. Barnes, Michelle Miniter, Rachel E. Hewitt, Clare E. Bryant, Huw D. Summers, Jonathan J. Powell, Paul Rees

Research output: Contribution to journalArticlepeer-review


Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study's objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows.

Original languageEnglish (US)
Article number100398
JournalCell Reports Methods
Issue number2
StatePublished - Feb 27 2023


  • 2D
  • 3D
  • CP: imaging
  • cell segmentation
  • confocal microscopy
  • digital pathology
  • immunofluorescence
  • label free
  • quantitative
  • single-cell
  • tissue

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Genetics
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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