Class-Controlling Copy-Paste Augmentation for Nuclear Segmentation

Abstract

Building segmentation models that can deal with rare and small nuclear objects in hematoxylin and eosin (H&E) stained pathologic images is a challenging task in digital pathology. Applying image augmentation can help alleviate this challenge. Hence, we propose new class-controlling copy-paste augmentation using a prepared nuclear objects set. Several image augmentations have been developed in computer vision for improving model performance; however, most of them are general-purpose methods and have not been designed for a specific domain. Our proposed method is appropriate for the pathology domain and provide strong regularization to make the model robust. In addition, it has the advantage of alleviating class imbalance problem, which is very common in histology datasets for nuclear segmentation. In our cross-validation experiments on a multi-tissue histology dataset, our method improves PQ and mPQ+ from 64.31 to 64.52 and 52.3 to 52.9, respectively.