  Many medical and biological protocols for analyzing individual biological
cells involve morphological evaluation based on cell staining, designed to
enhance imaging contrast and enable clinicians and biologists to differentiate
between various cell organelles. However, cell staining is not always allowed
in certain medical procedures. In other cases, staining may be time consuming
or expensive to implement. Here, we present a new deep-learning approach,
called HoloStain, which converts images of isolated biological cells acquired
without staining by holographic microscopy to their virtually stained images.
We demonstrate this approach for human sperm cells, as there is a
well-established protocol and global standardization for characterizing the
morphology of stained human sperm cells for fertility evaluation, but, on the
other hand, staining might be cytotoxic and thus is not allowed during human in
vitro fertilization (IVF). We use deep convolutional Generative Adversarial
Networks (DCGANs) with training that is based on both the quantitative phase
images and two gradient phase images, all extracted from the digital holograms
of the stain-free cells, with the ground truth of bright-field images of the
same cells that subsequently underwent chemical staining. To validate the
quality of our virtual staining approach, an experienced embryologist analyzed
the unstained cells, the virtually stained cells, and the chemically stained
sperm cells several times in a blinded and randomized manner. We obtained a
5-fold recall (sensitivity) improvement in the analysis results. With the
introduction of simple holographic imaging methods in clinical settings, the
proposed method has a great potential to become a common practice in human IVF
procedures, as well as to significantly simplify and facilitate other cell
analyses and techniques such as imaging flow cytometry.
