  The unsupervised segmentation is an increasingly popular topic in biomedical
image analysis. The basic idea is to approach the supervised segmentation task
as an unsupervised synthesis problem, where the intensity images can be
transferred to the annotation domain using cycle-consistent adversarial
learning. The previous studies have shown that the macro-level (global
distribution level) matching on the number of the objects (e.g., cells,
tissues, protrusions etc.) between two domains resulted in better segmentation
performance. However, no prior studies have exploited whether the unsupervised
segmentation performance would be further improved when matching the exact
number of objects at micro-level (mini-batch level). In this paper, we propose
a deep learning based unsupervised segmentation method for segmenting highly
overlapped and dynamic sub-cellular microvilli. With this challenging task,
both micro-level and macro-level matching strategies were evaluated. To match
the number of objects at the micro-level, the novel fluorescence-based
micro-level matching approach was presented. From the experimental results, the
micro-level matching did not improve the segmentation performance, compared
with the simpler macro-level matching.
