Automatic pancreas segmentation in abdominal CT images plays an important role in clinical applications. It can provide doctors with quantitative and qualitative information. Due to the small size, unclear edges, and the high anatomical differences between patients, it is a challenging task to accurately segment the pancreas with diseases. In this paper, we propose a new method to automatically segment the pancreas in abdominal CT images. First, we propose a contrast enhancement block. The block generates edge information and uses gating mechanism to enhance edge details of the pancreas. Second, we leverage a reverse attention block. This block utilizes the decoder feature map to guide the network to mine complementary discriminative regions. The proposed method is trained on 63 3D CT images, validated on 15 3D CT images, and tested on 28 3D CT images. Compared with manual segmentation, the mean Dice similarity coefficient can reach 86.11±8.02%. Experimental results show that our method can obtain more accurate segmentation results compared with existing segmentation methods.
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.
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