关于细胞核实例分割与分类的综述：利用上下文与注意力
A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attention
AI总结：
本文综述了H&E染色图像中基于上下文与注意力的细胞核实例分割与分类方法，并通过扩展Mask-RCNN和HoVer-Net进行对比实验，指出将病理学家的领域知识转化为算法设计仍具挑战性。
摘要：
从吉像素级的苏木精-伊红（H&E）染色全切片图像（WSIs）中手动标注细胞核是一项费时费力的任务，这意味着用于细胞核实例分割与分类的自动化算法可以减轻病理学家和临床研究人员的工作量，同时促进临床可解释特征的自动提取。但由于细胞核形态和颜色特征的高类内和类间变异性，以及H&E染色对伪影的易感性，最先进的算法无法以必要的性能正确检测和分类实例。在这项工作中，我们假设人工神经网络（ANNs）中的上下文和注意力归纳偏置可以提高细胞核实例分割和分类算法的泛化能力。我们对基于H&E染色显微成像的细胞核实例分割与分类的上下文和注意力方法进行了全面调查，同时对上下文和注意力所解决的挑战进行了深入讨论。此外，我们说明了当前方法的一些局限性，并提出了未来研究的想法。作为案例研究，我们用基于上下文和注意力的机制扩展了通用实例分割和分类方法（Mask-RCNN）和定制的细胞核实例分割和分类模型（HoVer-Net），并在多中心结肠细胞核识别和计数数据集上进行了比较分析。尽管病理学家在分析和标注WSIs时在多个层面上依赖上下文，同时关注特定的感兴趣区域（RoIs），但我们的研究结果表明，将这种领域知识转化为算法设计并非易事，要充分利用这些机制，应解决对这些方法的科学理解问题。
英文摘要：
Manually annotating nuclei from the gigapixel Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesise context and attention inductive biases in artificial neural networks (ANNs) could increase the generalization of algorithms for cell nuclei instance segmentation and classification. We conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general instance segmentation and classification method (Mask-RCNN) and a tailored cell nuclei instance segmentation and classification model (HoVer-Net) with context- and attention-based mechanisms, and do a comparative analysis on a multi-centre colon nuclei identification and counting dataset. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms, the scientific understanding of these methods should be addressed.
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