MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images

Cytometry A. 2022 Jun;101(6):521-528. doi: 10.1002/cyto.a.24541. Epub 2022 Feb 7.


Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM) that combines (a) a pipeline for cell segmentation and quantification that incorporates machine learning-based pixel classification to define cellular compartments, (b) a novel method for extending incomplete cell membranes, and (c) a deep learning-based cell shape descriptor. Using human colonic adenomas as an example, we show that MIRIAM is superior to widely utilized segmentation methods and provides a pipeline that is broadly applicable to different imaging platforms and tissue types.

Keywords: cell segmentation; image processing; multiplexed imaging; single cell analysis.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Cell Shape
  • Deep Learning*
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Machine Learning