Imaging fl ow cytometry and machine learning-based analysis

Abstract

Machine learning-based analysis has been recently applied to image data acquired in imaging flow cytometry technologies including both analyzers and sorters. Compared to analysis of conventional microscopy images, the largest difference appears when we have to complete the analysis within a limited time scale for accomplishing realtime analysis and subsequent cell sorting. In this article, we propose to categorize the analysis approaches into two groups based on the type of data modality, raw imaging signals or features explicitly extracted from images, being analyzed by a trained model. We hope that this review helps readers understand what kind of uniqueness, differences, and opportunities shows up depending on the way of implementing machine learning-based analysis in the recently developed “imaging” cell sorters.