Unsupervised high-throughput segmentation of cells and cell nuclei in
  quantitative phase images
Julia Sistermanns Ellen Emken Gregor Weirich Oliver Hayden Wolfgang Utschick
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. In an effort to make the information reasonable and
interpretable for cytopathologists, we also introduce new 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 per cell analysis time.