Semantic-guided Encoder Feature Learning for Blurry Boundary Delineation

TL;DR

This paper introduces a semantic-guided encoder feature learning strategy combined with a soft contour constraint to improve medical image segmentation, especially for blurry boundaries, achieving state-of-the-art results.

Contribution

It proposes a novel semantic-guided feature learning approach and a soft contour constraint mechanism to enhance boundary delineation in medical images.

Findings

Achieves state-of-the-art segmentation accuracy on clinical datasets.

Improves boundary localization in blurry regions.

Demonstrates good generalization on multiple datasets.

Abstract

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.