1. MIRIAM: A machine and deep learning single‐cell segmentation and quantification pipeline for multi‐dimensional tissue images
- Author
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Eliot T. McKinley, Justin Shao, Samuel T. Ellis, Cody N. Heiser, Joseph T. Roland, Mary C. Macedonia, Paige N. Vega, Susie Shin, Robert J. Coffey, and Ken S. Lau
- Subjects
Machine Learning ,Deep Learning ,Histology ,Image Processing, Computer-Assisted ,Humans ,Cell Biology ,Cell Shape ,Pathology and Forensic Medicine - Abstract
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.
- Published
- 2022