Nuclear segmentation within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow, due to the ability for nuclear features to act as key diagnostic markers. The development of automated methods for nuclear segmentation enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for automated nuclear segmentation that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. We demonstrate state-of-the-art performance compared to other methods on four independent multi-tissue histology image datasets. Furthermore, we propose an interpretable and reliable evaluation framework that effectively quantifies nuclear segmentation performance and overcomes the limitations of existing performance measures.
翻译:Haematoxylin 和 Eosin 内核分解图象是数字病理学工作流的一个根本先决条件,因为核特征能够作为关键的诊断标志。开发核分解自动化方法,能够在整片滑动病理图象中对数以万计核核进行定量分析,从而有可能进一步分析大规模核光度测定。然而,自动化核分解面临一个重大挑战,因为有几种不同类型的核核核分解,其中一些核核分解显示出诸如肿瘤细胞等大型的阶级内部变异性。此外,一些核核分解往往集中在一起。为了应对这些挑战,我们提出了一个新型的进化神经网络,用于自动核分解,利用在核像素与质量中心垂直和水平距离内所标定的丰富实例信息进行进一步分析。这些距离被用来将核分解分为不同的核心,导致准确的核分解,特别是在与重叠的事例中。我们展示了与可独立表现的状态性能和可克服的四分解模型的其他方法。我们展示了与可有效解读性能评估的其他方法。