Due to the wide existence and large morphological variances of nuclei,
accurate nuclei instance segmentation is still one of the most challenging
tasks in computational pathology. The annotating of nuclei instances, requiring
experienced pathologists to manually draw the contours, is extremely laborious
and expensive, which often results in the deficiency of annotated data. The
deep learning based segmentation approaches, which highly rely on the quantity
of training data, are difficult to fully demonstrate their capacity in this
area. In this paper, we propose a novel self-supervised learning framework to
deeply exploit the capacity of widely-used convolutional neural networks (CNNs)
on the nuclei instance segmentation task. The proposed approach involves two
sub-tasks (i.e., scale-wise triplet learning and count ranking), which enable
neural networks to implicitly leverage the prior-knowledge of nuclei size and
quantity, and accordingly mine the instance-aware feature representations from
the raw data. Experimental results on the publicly available MoNuSeg dataset
show that the proposed self-supervised learning approach can remarkably boost
the segmentation accuracy of nuclei instance---a new state-of-the-art average
Aggregated Jaccard Index (AJI) of 70.63%, is achieved by our self-supervised
ResUNet-101. To our best knowledge, this is the first work focusing on the
self-supervised learning for instance segmentation.

本文提出了一种自监督学习框架，通过对卷积神经网络进行尺度三元组学习和数量排序等两个子任务，隐式地利用细胞核大小和数量的先验知识，从原始数据中挖掘出特征表示，最终在公开数据集上取得了非常好的结果，大大提高了细胞核实例分割的准确性。