Neutrophils identification by deep learning and Voronoi diagram of clusters

Abstract

Neutrophils are a primary type of immune cells, and their identification is critical in clinical diagnosis of active inflammation. However, in H&E histology tissue slides, the appearances of neutrophils are highly variable due to morphology, staining and locations. Further, the noisy and complex tissue environment causes artifacts resembling neutrophils. Thus, it is challenging to design, in a hand-crafted manner, computerized features that help identify neutrophils effectively. To better characterize neutrophils, we propose to extract their features in a learning manner, by constructing a deep convolutional neural network (CNN). In addition, in clinical practice, neutrophils are identified not only based on their individual appearance, but also on the context formed by multiple related cells. It is not quite straightforward for deep learning to capture precisely the rather complex cell context. Hence, we further propose to combine deep learning with Voronoi diagram of clusters (VDC), to extract needed context. Experiments on clinical data show that (1) the learned hierarchical representation of features by CNN outperforms hand-crafted features on characterizing neutrophils, and (2) the combination of CNN and VDC significantly improves over the state-of-the-art methods for neutrophil identification on H&E histology tissue images.