  Encoder-decoder architectures are widely adopted for medical image
segmentation tasks. With the lateral skip connection, the models can obtain and
fuse both semantic and resolution information in deep layers to achieve more
accurate segmentation performance. However, in many applications (e.g., blurry
boundary images), these models often cannot precisely locate complex boundaries
and segment tiny isolated parts. To solve this challenging problem, we firstly
analyze why simple skip connections are not enough to help accurately locate
indistinct boundaries and argue that it is due to the fuzzy information in the
skip connection provided in the encoder layers. Then we propose a
semantic-guided encoder feature learning strategy to learn both high resolution
and rich semantic encoder features so that we can more accurately locate the
blurry boundaries, which can also enhance the network by selectively learning
discriminative features. Besides, we further propose a soft contour constraint
mechanism to model the blurry boundary detection. Experimental results on real
clinical datasets show that our proposed method can achieve state-of-the-art
segmentation accuracy, especially for the blurry regions. Further analysis also
indicates that our proposed network components indeed contribute to the
improvement of performance. Experiments on additional datasets validate the
generalization ability of our proposed method.
