Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL). Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible, and we hope to leverage these external datasets to assist nucleus instance segmentation on the target dataset which only has very limited annotation. To achieve this goal, we adopt the meta-learning based FSL paradigm, which however has to be tailored in two substantial aspects before adapting to our task. First, since the novel classes may be inconsistent with those of the external dataset, we extend the basic definition of few-shot instance segmentation (FSIS) to generalized few-shot instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of nucleus segmentation, including touching between adjacent cells, cellular heterogeneity, etc., we further introduce a structural guidance mechanism into the GFSIS network, finally leading to a unified Structurally-Guided Generalized Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a couple of publicly accessible datasets demonstrate that, SGFSIS can outperform other annotation-efficient learning baselines, including semi-supervised learning, simple transfer learning, etc., with comparable performance to fully supervised learning with less than 5% annotations.

翻译：从组织病理学图像中进行细胞核实例分割面临标注工作极其繁琐且依赖专家经验的挑战。作为该任务的有前景的解决方案，标注高效深度学习范式（如弱监督/半监督学习、生成对抗学习等）近年来吸引了大量研究兴趣。本文从少样本学习（FSL）视角提出了一种标注高效的细胞核实例分割方法。我们的工作源于以下动机：随着计算病理学的蓬勃发展，越来越多的全标注数据集公开可用，我们希望利用这些外部数据集来辅助仅具有极少标注的目标数据集上的细胞核实例分割。为实现该目标，我们采用基于元学习的FSL范式，但需针对该任务进行两大实质性改进：首先，由于新类别可能与外部数据集类别不一致，我们将少样本实例分割（FSIS）的基本定义扩展为广义少样本实例分割（GFSIS）；其次，为应对细胞核分割固有的挑战（如相邻细胞接触、细胞异质性等），我们进一步在GFSIS网络中引入结构引导机制，最终形成统一的引导式广义少样本实例分割（SGFSIS）框架。在多个公开数据集上的大量实验表明，SGFSIS可优于其他标注高效学习基线（包括半监督学习、简单迁移学习等），在仅使用不到5%标注的情况下达到与全监督学习相当的性能。