  Nuclei Segmentation from histology images is a fundamental task in digital
pathology analysis. However, deep-learning-based nuclei segmentation methods
often suffer from limited annotations. This paper proposes a realistic data
augmentation method for nuclei segmentation, named InsMix, that follows a
Copy-Paste-Smooth principle and performs morphology-constrained generative
instance augmentation. Specifically, we propose morphology constraints that
enable the augmented images to acquire luxuriant information about nuclei while
maintaining their morphology characteristics (e.g., geometry and location). To
fully exploit the pixel redundancy of the background and improve the model's
robustness, we further propose a background perturbation method, which randomly
shuffles the background patches without disordering the original nuclei
distribution. To achieve contextual consistency between original and template
instances, a smooth-GAN is designed with a foreground similarity encoder (FSE)
and a triplet loss. We validated the proposed method on two datasets, i.e.,
Kumar and CPS datasets. Experimental results demonstrate the effectiveness of
each component and the superior performance achieved by our method to the
state-of-the-art methods.
