Gland segmentation via dual encoders and boundary-enhanced attention
Huadeng Wang Jiejiang Yu Bingbing Li Xipeng Pan Zhenbing Liu Rushi Lan Xiaonan Luo
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.