  A high-performance unsupervised nuclei instance segmentation (HUNIS) method
is proposed in this work. HUNIS consists of two-stage block-wise operations.
The first stage includes: 1) adaptive thresholding of pixel intensities, 2)
incorporation of nuclei size/shape priors and 3) removal of false positive
nuclei instances. Then, HUNIS conducts the second stage segmentation by
receiving guidance from the first one. The second stage exploits the
segmentation masks obtained in the first stage and leverages color and shape
distributions for a more accurate segmentation. The main purpose of the
two-stage design is to provide pixel-wise pseudo-labels from the first to the
second stage. This self-supervision mechanism is novel and effective.
Experimental results on the MoNuSeg dataset show that HUNIS outperforms all
other unsupervised methods by a substantial margin. It also has a competitive
standing among state-of-the-art supervised methods.
