  Generally, microscopy image analysis in biology relies on the segmentation of
individual nuclei, using a dedicated stained image, to identify individual
cells. However stained nuclei have drawbacks like the need for sample
preparation, and specific equipment on the microscope but most importantly, and
as it is in most cases, the nuclear stain is not relevant to the biological
questions of interest but is solely used for the segmentation task. In this
study, we used non-stained brightfield images for nuclei segmentation with the
advantage that they can be acquired on any microscope from both live or fixed
samples and do not necessitate specific sample preparation. Nuclei semantic
segmentation from brightfield images was obtained, on four distinct cell lines
with U-Net-based architectures. We tested systematically deep pre-trained
encoders to identify the best performing in combination with the different
neural network architectures used. Additionally, two distinct and effective
strategies were employed for instance segmentation, followed by thorough
instance evaluation. We obtained effective semantic and instance segmentation
of nuclei in brightfield images from standard test sets as well as from very
diverse biological contexts triggered upon treatment with various small
molecule inhibitor. The code used in this study was made public to allow
further use by the community.
