Applied Tumor Immunity Clinical Cooperation Unit, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany; Department of Informatics, Technical University Federico Santa María, Valparaiso, Chile.
Statistical Physics and Theoretical Biophysics Group, Institute for Theoretical Physics, Heidelberg University, Heidelberg, Germany.
Med Image Anal. 2017 May;38:90-103. doi: 10.1016/j.media.2017.02.009. Epub 2017 Mar 6.
The segmentation of cell nuclei is an important step towards the automated analysis of histological images. The presence of a large number of nuclei in whole-slide images necessitates methods that are computationally tractable in addition to being effective. In this work, a method is developed for the robust segmentation of cell nuclei in histological images based on the principles of persistent homology. More specifically, an abstract simplicial homology approach for image segmentation is established. Essentially, the approach deals with the persistence of disconnected sets in the image, thus identifying salient regions that express patterns of persistence. By introducing an image representation based on topological features, the task of segmentation is less dependent on variations of color or texture. This results in a novel approach that generalizes well and provides stable performance. The method conceptualizes regions of interest (cell nuclei) pertinent to their topological features in a successful manner. The time cost of the proposed approach is lower-bounded by an almost linear behavior and upper-bounded by O(n) in a worst-case scenario. Time complexity matches a quasilinear behavior which is O(n) for ε < 1. Images acquired from histological sections of liver tissue are used as a case study to demonstrate the effectiveness of the approach. The histological landscape consists of hepatocytes and non-parenchymal cells. The accuracy of the proposed methodology is verified against an automated workflow created by the output of a conventional filter bank (validated by experts) and the supervised training of a random forest classifier. The results are obtained on a per-object basis. The proposed workflow successfully detected both hepatocyte and non-parenchymal cell nuclei with an accuracy of 84.6%, and hepatocyte cell nuclei only with an accuracy of 86.2%. A public histological dataset with supplied ground-truth data is also used for evaluating the performance of the proposed approach (accuracy: 94.5%). Further validations are carried out with a publicly available dataset and ground-truth data from the Gland Segmentation in Colon Histology Images Challenge (GlaS) contest. The proposed method is useful for obtaining unsupervised robust initial segmentations that can be further integrated in image/data processing and management pipelines. The development of a fully automated system supporting a human expert provides tangible benefits in the context of clinical decision-making.
细胞核分割是自动分析组织学图像的重要步骤。全切片图像中存在大量细胞核，这就需要一种计算上可行且有效的方法。在这项工作中，基于持久同调原理，开发了一种用于组织学图像中细胞核稳健分割的方法。更具体地说，建立了一种用于图像分割的抽象单纯同调方法。本质上，该方法处理图像中不连通集的持久性，从而识别表示持久性模式的显著区域。通过引入基于拓扑特征的图像表示，分割任务不太依赖于颜色或纹理的变化。这导致了一种泛化能力强且性能稳定的新方法。该方法成功地将与拓扑特征相关的感兴趣区域（细胞核）概念化。所提出方法的时间复杂度下限为几乎线性，最坏情况下上限为 O(n)。时间复杂度与 quasilinear 行为匹配，对于 ε < 1，时间复杂度为 O(n)。使用来自肝组织学切片的图像作为案例研究来演示该方法的有效性。组织学景观由肝细胞和非实质细胞组成。所提出方法的准确性通过与由传统滤波器组输出创建的自动化工作流程（由专家验证）和随机森林分类器的监督训练进行比较来验证。结果是基于每个对象获得的。所提出的工作流程成功地检测了肝细胞和非实质细胞的细胞核，准确率为 84.6%，仅检测肝细胞的细胞核，准确率为 86.2%。还使用带有提供的真实数据的公共组织学数据集来评估所提出方法的性能（准确率：94.5%）。进一步的验证是使用公共数据集和结肠组织学图像中腺体分割挑战赛（GlaS）的真实数据进行的。所提出的方法可用于获得无监督的稳健初始分割，这些分割可以进一步集成到图像/数据处理和管理管道中。支持人类专家的全自动系统的开发在临床决策方面具有切实的益处。
