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MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue
Eliot T McKinley1,2, Justin Shao1,2, Samuel T Ellis1
1Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Summary
We developed Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM), a new tool for cell segmentation in tissue imaging. MIRIAM improves accuracy for single-cell protein profiling in complex biological samples.
Area of Science:
- Computational biology
- Biomedical imaging
- Pathology
Background:
- Multiplexed tissue imaging enables single-cell protein profiling.
- Accurate cell segmentation is crucial but challenging for tissue sections.
- Existing segmentation tools often lack robustness.
Purpose of the Study:
- To introduce Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM), a novel cell segmentation pipeline.
- To address limitations in current cell segmentation methods for multiplexed tissue imaging.
- To provide a broadly applicable solution for tissue section analysis.
Main Methods:
- Developed a machine learning-based pixel classification pipeline for cellular compartment definition.
- Implemented a novel method for extending incomplete cell membranes.
- Integrated a deep learning-based cell shape descriptor.
- Applied the pipeline to human colonic adenomas.
Main Results:
- MIRIAM demonstrated superior performance compared to widely used segmentation methods.
- The pipeline effectively segments cells and quantifies protein expression at the single-cell level.
- Validated the method's applicability across different imaging platforms and tissue types.
Conclusions:
- MIRIAM offers a robust and accurate solution for cell segmentation in multiplexed tissue imaging.
- This tool enhances the analysis of protein expression in complex tissue architectures.
- MIRIAM is a versatile pipeline applicable to diverse research and clinical settings.
