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Generally, microscopy image analysis in biology relies on the segmentation ofindividual nuclei, using a dedicated stained image, to identify individualcells. However stained nuclei have drawbacks like the need for samplepreparation, and specific equipment on the microscope but most importantly, andas it is in most cases, the nuclear stain is not relevant to the biologicalquestions of interest but is solely used for the segmentation task. In thisstudy, we used non-stained brightfield images for nuclei segmentation with theadvantage that they can be acquired on any microscope from both live or fixedsamples and do not necessitate specific sample preparation. Nuclei semanticsegmentation from brightfield images was obtained, on four distinct cell lineswith U-Net-based architectures. We tested systematically deep pre-trainedencoders to identify the best performing in combination with the differentneural network architectures used. Additionally, two distinct and effectivestrategies were employed for instance segmentation, followed by thoroughinstance evaluation. We obtained effective semantic and instance segmentationof nuclei in brightfield images from standard test sets as well as from verydiverse biological contexts triggered upon treatment with various smallmolecule inhibitor. The code used in this study was made public to allowfurther use by the community.

BriFiSeg: a deep learning-based method for semantic and instance segmentation of nuclei in brightfield images