Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform
This provides an incremental improvement for pathologists and researchers in medical imaging by automating cell phenotyping with high accuracy.
The paper tackles automated cell classification in tissue samples using multiplexed immunofluorescence images, achieving average accuracies above 95% for immune cells in cancer and brain cells in neurological degenerative tissue.
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%.