Yiru Liu
ABSTRACT
Accurate medical image segmentation is essential to achieve precise medical image analysis, e.g., blood vessel detection or lung segmentation, which has attracted researchers’ attention. So far, state-of-the-art techniques improving segmentation results have extracted the multi-scale contextual features with dilated convolution. However, these techniques cannot capture sufficient scale information due to only using several parallel independent branches, which result in leaving a gap between existed and ideal segmentation results.To address this problem, we design a hierarchical context integration network (HC-Net) which includes encoder module, hierarchical context integration module (HCM), and decoder module. Our HCM can not only extract the scale features in a single branch, but also learn the scale correlation among different branches. Meanwhile, self-attention mechanism therein is utilized to integrate context features and global dependencies adaptively for decoder Module. The proposed HC-Net has been applied two popular datasets, DRIVE and LUNA, and the experimental results show that our method outperforms state-of-the-art ones.
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Index Terms
- HC-Net: Hierarchical Context integration Network for medical image segmentation
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