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Marked Point Processes with Simple and Complex Shape Objects for Cell Nuclei Extraction from Breast Cancer HE Images

机译:具有简单和复杂形状对象的标记点过程用于从乳腺癌H&E图像中提取细胞核

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According to the Nottingham Grading System for breast cancer grading, nuclear pleomorphism is one of the three criteria along with tubule formation and mitotic count taken into account in grading procedure. Nuclear pleomorphism is largely based on the information about variation of nuclei appearance, size, and shape. Nuclei extraction from breast cancer images is thus necessary for cancer grading, and has become one of the major problem in the domain of automatic image analysis. Recently, several papers have shown that stochastic Marked Point Processes are a promising tool for dealing with this kind of problems. In this paper, we will present visual and quantitative comparisons of results obtained with two Marked Point Process based models with two types of objects used, and analyse the advantages of each of them. We will first show a way to detect nuclei position and size using ellipse-shaped objects. Ellipses give a good approximation of nuclei shape size in a fast way. We will then use arbitrarily-shaped objects to delineate more precisely nuclei contours. As this method is a data driven method, we will discuss the best data energy to use for each kind of objects, based on common criteria of the nuclei in any cancer grade. Results are obtained using Haematoxylin and Eosin (H&E) stained breast cancer slide images. As appearance, size and shape may vary a lot depending on the cancer grade, we will present results for different grades and compare our methods for each of them. The quantitative quality of obtained results will be shown vie comparing with a ground truth segmentations given by pathologists.
机译:根据用于乳腺癌分级的诺丁汉分级系统,核多型性是在分级过程中考虑的三个标准之一,此外还有肾小管形成和有丝分裂计数。核多态性主要基于有关核外观,大小和形状变化的信息。因此,从乳腺癌图像中提取核对于癌症分级是必要的,并且已经成为自动图像分析领域中的主要问题之一。最近,几篇论文表明,随机标记点过程是解决此类问题的有前途的工具。在本文中,我们将对使用两种基于对象的两种基于标记点过程的模型获得的结果进行可视化和定量比较,并分析它们各自的优势。我们将首先展示一种使用椭圆形物体检测核位置和大小的方法。椭圆以快速方式很好地近似了原子核的形状大小。然后,我们将使用任意形状的对象来更精确地描绘核轮廓。由于此方法是一种数据驱动的方法,因此,我们将根据任何癌症级别中细胞核的通用标准,讨论可用于每种对象的最佳数据能量。使用苏木精和曙红(H&E)染色的乳腺癌玻片图像获得结果。由于外观,大小和形状可能会因癌症级别而有很大差异,因此我们将介绍不同级别的结果,并比较每种方法的结果。与病理学家给出的基本事实分割相比较,将显示获得的结果的定量质量。

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