A Deep Learning Approach for Semantic Segmentation in Histology Tissue Images

To make reliable diagnosis, pathologists often need to identify certain special regions in medical images. In inflammatory bowel disease (IBD) diagnosis via histology tissue image examination, muscle regions are known to have no immune cell infiltration, and thus are ignored by pathologists. Also, messy regions (e.g., due to distortion and poor staining) are low in diagnostic yield. Hence, excluding muscle and messy regions to focus on vital regions is crucial for accurate diagnosis of IBD. In this paper, we propose a novel deep neural network based on fully convolutional networks (FCN) to identify muscle and messy regions, in an end-to-end fashion. First, we address the challenge of having limited medical training data, for training our deep neural network (a common problem for medical image processing, which may impede the application of the powerful deep learning method). Second, to deal with target regions of largely different sizes and arbitrary shapes, our deep neural network explores multi-scale information and structural information. Experimental results on clinical images show that our approach outperforms the state-of-the-art FCN for semantic segmentation of muscle and messy regions. Our approach may be readily extended to identify other types of regions in a variety of medical imaging applications.