Unsupervised high-throughput segmentation of cells and cell nuclei in quantitative phase images

In the effort to aid cytologic diagnostics by establishing automatic single-cell screening using high-throughput digital holographic microscopy for clinical studies thousands of images and millions of cells are captured. The bottleneck lies in an automatic, fast, and unsupervised segmentation technique that does not limit the types of cells which might occur. We propose an unsupervised multistage method that segments correctly without confusing noise or reflections with cells and without missing cells that also includes the detection of relevant inner structures, especially the cell nucleus in the unstained cell. To make the information reasonable and interpretable for cytopathologists, we designed and collected cytoplasmic and nuclear features of potential help for cytologic diagnoses which exploit the quantitative phase information inherent to the measurement scheme. We show that the segmentation provides consistently good results over many experiments on patient samples in a reasonable percell analysis time.