Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform

• URL: http://arxiv.org/abs/2007.09471v1
• Date: Sat, 18 Jul 2020 16:45:32 GMT
• Title: Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform
• Authors: Alberto Santamaria-Pang, Anup Sood, Dan Meyer, Aritra Chowdhury, Fiona Ginty
• Abstract summary: 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.
• Score: 0.5599792629509229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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