High-resolution microscopy images of tissue specimens provide detailed
information about the morphology of normal and diseased tissue. Image analysis
of tissue morphology can help cancer researchers develop a better understanding
of cancer biology. Segmentation of nuclei and classification of tissue images
are two common tasks in tissue image analysis. Development of accurate and
efficient algorithms for these tasks is a challenging problem because of the
complexity of tissue morphology and tumor heterogeneity. In this paper we
present two computer algorithms; one designed for segmentation of nuclei and
the other for classification of whole slide tissue images. The segmentation
algorithm implements a multiscale deep residual aggregation network to
accurately segment nuclear material and then separate clumped nuclei into
individual nuclei. The classification algorithm initially carries out
patch-level classification via a deep learning method, then patch-level
statistical and morphological features are used as input to a random forest
regression model for whole slide image classification. The segmentation and
classification algorithms were evaluated in the MICCAI 2017 Digital Pathology
challenge. The segmentation algorithm achieved an accuracy score of 0.78. The
classification algorithm achieved an accuracy score of 0.81.

本文提出了两个计算机算法，分别用于分割细胞核和分类组织切片图像。采用多尺度深度残差聚合网络实现核物质的准确分割，采用深度学习方法与随机森林回归模型相结合进行图像分类。该算法在 MICCAI 2017 数字病理学挑战赛中取得了较高的精度得分。