Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods.

翻译：在病理图像上实现准确且自动化的腺体分割，可辅助病理学家诊断结直肠腺癌的恶性程度。然而，由于腺体形态多样、恶性腺体严重变形，以及腺体间存在重叠粘连，腺体分割始终极具挑战性。为解决这些问题，我们提出了一种DEA模型。该模型由两个分支组成：骨干编码-解码网络与局部语义提取网络。骨干编码-解码网络提取高级语义特征，利用所提出的特征解码器恢复特征空间信息，随后通过边界增强注意力机制增强腺体边界特征。局部语义提取网络采用预训练的DeepLabv3+作为局部语义引导编码器，实现边缘特征的提取。在两个公开数据集GlaS和CRAG上的实验结果表明，我们的方法性能优于其他腺体分割方法。