Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin 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 for artificial intelligence (AI) tools. 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 hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then 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 (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting.

中文翻译：

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细胞核实例分割和分类调查：利用上下文和注意力


核衍生的形态学特征和生物标志物提供了有关肿瘤微环境的相关见解，同时也允许对特定癌症类型进行诊断和预后。然而，从十亿像素苏木精和伊红 （H&E） 染色的全玻片图像 （WSI） 中手动注释细胞核是一项费力且昂贵的任务，这意味着用于细胞核实例分割和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，同时促进人工智能 （AI） 工具临床可解释特征的自动提取。但是，由于细胞核形态和色度特征的类内和类间高度可变性，以及 H&E 染色对伪影的敏感性，最先进的算法无法正确检测和分类具有必要性能的实例。在这项工作中，我们假设人工神经网络 （ANN） 中的上下文和注意力归纳偏差可以提高细胞核实例分割和分类算法的性能和泛化。为了了解实例分割和分类中基于上下文和注意力的机制的优势、用例和局限性，我们首先回顾了计算机视觉和医学成像方面的工作。然后，我们对 H&E 染色显微镜成像中细胞核实例分割和分类的背景和注意力方法进行了彻底的调查，同时全面讨论了通过背景和注意力应对的挑战。此外，我们说明了当前方法的一些局限性，并为未来的研究提出了想法。 作为一个案例研究，我们扩展了通用 （Mask-RCNN） 和定制的 （HoVer-Net） 实例分割和分类方法，具有基于上下文和注意力的机制，并在用于结肠核识别和计数的多中心数据集上进行了比较分析。