CRU-Net: A Deep Learning Network for Semantic Segmentation of Pathological Tissue Slices

Abstract

The study of cell nuclei is the starting point of modern medical pathology analysis and new drug development, and the semantic segmentation of pathological tissue slice images is a fundamental task of cell nucleus research[1]. This paper proposes a deep learning convolutional neural network for semantic segmentation of cell nuclei, where V-Net [6] is used as the basic framework for segmentation, and then the channel attention mechanism is added to its skip connections. The experiment is evaluated on the dataset of pathological tissue slice images, publicly released in the 2018 Kaggle Challenge data science bowl. The experimental results show that the improved deep learning convolutional neural network achieves excellent performance on the semantic segmentation task of pathological tissue slice images, and can be used as a tool for automatic segmentation of pathological tissue slice images.