Cellular community detection for tissue phenotyping in colorectal cancer histology images
Cellular community detection for tissue phenotyping in colorectal cancer histology images
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DOI：
10.1016/j.media.2020.101696
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发表时间：
2020-07-01
影响因子：
10.9
通讯作者：
Rajpoot, Nasir
中科院分区：
文献类型：
--
作者：
Javed, Sajid;Mahmood, Arif;Rajpoot, Nasir
基于细胞组成对癌症组织学图像中的各种类型的组织进行分类是朝着开发用于空间肿瘤微环境的系统数字分析的计算病理学工具迈出的重要一步。大多数现有的组织表型分析方法仅限于肿瘤和间质的分类，并且需要大量的注释组织学图像，而这些图像通常是不可用的。在目前的工作中，我们提出的问题，确定不同的组织表型，在细胞图或网络中找到社区。首先，我们训练了一个深度神经网络，用于细胞检测和分类为五种不同的细胞成分。将检测到的细胞核视为节点，使用Delaunay三角测量分配潜在的细胞-细胞连接，从而产生细胞级图。基于该细胞图，计算捕获不同类型细胞的潜在细胞间连接的特征向量。这些特征向量用于构建基于卡方距离的块级图。我们将补丁级节点映射到几何空间，将每个节点表示为网络中其他节点的测地距离向量，并在正密度梯度方向上迭代地将补丁节点向最大密度区域漂移。所提出的算法进行评估，公开可用的数据集和另一个新的大规模数据集组成的280 K补丁的七个组织表型。经专家病理学家验证，估计的群落具有重要的生物学意义。与当前最先进的方法的比较揭示了组织表型的显著性能改善。(C)2020爱思唯尔B. V.保留所有权利。
Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping. (C) 2020 Elsevier B.V. All rights reserved.