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.
Automatic lung segmentation with severe pathology plays a significant role in the clinical application, which can save physicians’ efforts to annotate lung anatomy. Since the lung has fuzzy boundary in low-dose computed tomography (CT) images, and the tracheas and other tissues generally have the similar gray value as the lung, it is a challenging task to accurately segment lung. How to extract key features and remove background features is a core problem for lung segmentation. This paper introduces a novel approach for automatic segmentation of lungs in low-dose CT images. First, we propose a contrastive attention module, which generates a pair of foreground and background attention maps to guide feature learning of lung and background separately. Second, a triplet loss is used on three feature vectors from different regions to pull the features from the full image and the lung region close whereas pushing the features from background away. Our method was validated on a clinical data set of 78 CT scans using the four-fold cross validation strategy. Experimental results showed that our method achieved more accurate segmentation results than that of state-of-the-art approaches.
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