We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images. The method utilizes multiple markers stained in situ on a single tissue section on a robust hyperplex immunofluorescence platform (Cell DIVE, GE Healthcare) that provides multi-channel images allowing analysis at single cell/sub-cellular levels. The cell classification method consists of two steps: first, an automated training set from every image is generated using marker-to-cell staining information. This mimics how a pathologist would select samples from a very large cohort at the image level. In the second step, a probability model is inferred from the automated training set. The probabilistic model captures staining patterns in mutually exclusive cell types and builds a single probability model for the data cohort. We have evaluated the proposed approach to classify: i) immune cells in cancer and ii) brain cells in neurological degenerative diseased tissue with average accuracies above 95%.
@article{arxiv.2007.09471,
title = {Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform},
author = {Alberto Santamaria-Pang and Anup Sood and Dan Meyer and Aritra Chowdhury and Fiona Ginty},
journal= {arXiv preprint arXiv:2007.09471},
year = {2020}
}
Comments
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)