Gland segmentation in pancreas histopathology images based on selective multi-scale attention

Abstract

Pathology is an important subject in the treatment of pancreatic cancer. The tumor presented in the pathological images includes not only the tumor cells, but also the surrounding background structures. Automatic and accurate gland segmentation in histopathology images plays a significant role for cancer diagnosis and clinical application, which assist pathologists to diagnose the malignancy degree of pancreas caner. Due to the large variability of size and shape in glandular appearance and the heterogeneity between different cells, it is a challenging task to accurately segment glands in histopathology images. In this paper, a selective multi-scale attention (SMA) block is proposed for gland segmentation. First, a selection unit is used between the encoder and decoder to select features by amplifying effective information and suppressing redundant information according to a factor obtained during training. Second, we propose a multi-scale attention module to fuse feature maps at different scales. Our method is validated on a dataset of 200 images of size 512×512 from 24 H&E stained pancreas histological images. Experimental results show that our method achieves more accurate segmentation results than that of state-of-the-art approaches.