Fouad Shereen, Randell David, Galton Antony, Mehanna Hisham, Landini Gabriel
School of Dentistry, Institute of Clinical Sciences, University of Birmingham, Birmingham, United Kingdom.
Department of Computer Science, University of Exeter, Exeter, United Kingdom.
PLoS One. 2017 Nov 30;12(11):e0188717. doi: 10.1371/journal.pone.0188717. eCollection 2017.
Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary 'virtual-cells', each enclosing a potential 'nucleus' (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms.
在数字化组织病理图像中对组织的组织学相关区域进行算法分割是迈向计算机辅助诊断和分析的关键一步。例如，在图像中自动识别上皮组织和基质组织对于肿瘤微环境分析和表征中的空间定位及引导非常重要。当前的分割方法基于监督方法，这需要来自高质量、手动标注图像的大量训练数据。而获取这些数据通常既困难又昂贵。本文提出了一种基于口咽癌组织微阵列（TMA）无监督分割的独立于数据的替代框架。首先将基于数学形态学的自动分割算法应用于苏木精和伊红染色的光学显微镜图像。这将图像划分为多个二进制“虚拟细胞”，每个虚拟细胞包围一个潜在的“细胞核”（苏木精吸收图像中的暗盆）。从这些虚拟细胞及其包围的细胞核获得的颜色和形态测量值被输入到一个先进的无监督学习模型中，用于识别上皮组织和基质组织。在这里，我们利用两种一致性聚类（CC）算法对组织区域进行无监督识别，该算法考虑了一组个体聚类算法的一致意见。与大多数依赖单一聚类方法的无监督分割分析不同，CC学习模型允许更稳健和稳定地检测组织区域。所提出的框架性能已在55张手动标注的口咽组织图像上进行了评估。所提出的分割算法的定性和定量结果与八种流行的组织分割策略相比具有优势。此外，这里获得的无监督结果优于使用个体聚类算法获得的结果。
