  Recently deep neural networks, which require a large amount of annotated
samples, have been widely applied in nuclei instance segmentation of H\&E
stained pathology images. However, it is inefficient and unnecessary to label
all pixels for a dataset of nuclei images which usually contain similar and
redundant patterns. Although unsupervised and semi-supervised learning methods
have been studied for nuclei segmentation, very few works have delved into the
selective labeling of samples to reduce the workload of annotation. Thus, in
this paper, we propose a novel full nuclei segmentation framework that chooses
only a few image patches to be annotated, augments the training set from the
selected samples, and achieves nuclei segmentation in a semi-supervised manner.
In the proposed framework, we first develop a novel consistency-based patch
selection method to determine which image patches are the most beneficial to
the training. Then we introduce a conditional single-image GAN with a
component-wise discriminator, to synthesize more training samples. Lastly, our
proposed framework trains an existing segmentation model with the above
augmented samples. The experimental results show that our proposed method could
obtain the same-level performance as a fully-supervised baseline by annotating
less than 5% pixels on some benchmarks.
