Self-supervised Pre-training for Nuclei Segmentation

Abstract

The accurate segmentation of nuclei is crucial for cancer diagnosis and further clinical treatments. For semantic segmentation of nuclei, Vision Transformers (VT) have the potentiality to outperform Convolutional Neural Network (CNN) based models due to their ability to model long-range dependencies (i.e., global context). Usually, VT and CNN models are pre-trained with large-scale natural image dataset (i.e., ImageNet) in fully-supervised manner. However, pre-training nuclei segmentation models with ImageNet is not much helpful because of morphological and textural differences between natural image domain and medical image domain. Also, ImageNet-like large-scale annotated histology dataset rarely exists in medical image domain. In this paper, we propose a novel region-level Self-Supervised Learning (SSL) approach and corresponding triplet loss for pre-training semantic nuclei segmentation model with unannotated histology images extracted from Whole Slide Images (WSI). Our proposed region-level SSL is based on the observation that, non-background (i.e., nuclei) patches of an input image are difficult to predict from surrounding neighbor patches, and vice versa. We empirically demonstrate the superiority of our proposed SSL incorporated VT model on two public nuclei segmentation datasets.