  Segmentation is a fundamental task in medical image analysis. However, most
existing methods focus on primary region extraction and ignore edge
information, which is useful for obtaining accurate segmentation. In this
paper, we propose a generic medical segmentation method, called Edge-aTtention
guidance Network (ET-Net), which embeds edge-attention representations to guide
the segmentation network. Specifically, an edge guidance module is utilized to
learn the edge-attention representations in the early encoding layers, which
are then transferred to the multi-scale decoding layers, fused using a weighted
aggregation module. The experimental results on four segmentation tasks (i.e.,
optic disc/cup and vessel segmentation in retinal images, and lung segmentation
in chest X-Ray and CT images) demonstrate that preserving edge-attention
representations contributes to the final segmentation accuracy, and our
proposed method outperforms current state-of-the-art segmentation methods. The
source code of our method is available at https://github.com/ZzzJzzZ/ETNet.
