Figure 1: Label-free imaging flow cytometry workflow.

First the brightfield and darkfield images of the cells are measured by an imaging flow cytometer. The brightfield and darkfield images depict the light transmitted through the cell and light scattered from the cells within a cone centered at a 90° angle, respectively. Then the images are preprocessed, where we reshape the images to have their sizes coincide and tile them to montages of 15 × 15 images. The montages are loaded into the open-source image software CellProfiler that we use to segment the cells’ brightfield images and to extract morphological features from the images. Finally, we apply supervised machine learning such as classification. For this purpose we need an annotated set of cells where the actual cell state is known to train the classifier and to test its predictive power. Once the classifier is trained it is used to predict the state of unlabelled cells and to digitally sort the cells into bins.