Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods.

翻译：细胞核分类为组织病理学图像分析提供了重要信息。然而，不同细胞核类型在外观上的巨大差异给识别带来了困难。大多数基于神经网络的方法受到卷积局部感受野的影响，较少关注细胞核的空间分布或不规则轮廓形状。本文首先提出一种新颖的多边形结构特征学习机制，将细胞核轮廓转化为按顺序采样的点序列，并利用循环神经网络聚合关键点之间距离的序列变化，以获得可学习的形状特征。其次，将组织病理学图像转化为以细胞核为节点的图结构，构建图神经网络将细胞核的空间分布嵌入其表征中。为捕捉细胞核类别与其周围组织模式之间的相关性，我们进一步引入边缘特征，定义为相邻细胞核之间的背景纹理。最后，将多边形与图结构学习机制整合到一个统一框架中，该框架可提取细胞核内与细胞核间的结构特征用于细胞核分类。实验结果表明，与现有最先进方法相比，所提框架取得了显著性能提升。