For the example of digital holographic microscopy (DHM) we explored strategies to discriminate adherent and suspended single cells utilizing biophysical parameters retrieved label-free from DHM quantitative phase images in combination with machine learning (ML). Quantitative DHM phase contrast images of adherent cells were segmented while suspended single cells were analyzed based on a two-dimensional fitting approach. The retrieved parameter clouds were subsequently evaluated with different ML algorithms with the aim of an intuitive and user-friendly data representation. The results of the study demonstrate that our approach is capable for reliable discrimination between different cell types and to distinguish between different phenotypes.
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