High- and Low-Level Feature Enhancement for Medical Image Segmentation

The fully convolutional networks (FCNs) have achieved state-of-the-art performance in numerous medical image segmentation tasks. Most FCNs typically focus on fusing features in different levels to improve the learning ability to multi-scale features. In this paper, we explore an alternative direction to improve network performance by enhancing the encoding quality of high- and low-level features, so as to introduce two feature enhancement modules: (i) high-level feature enhancement module (HFE); (ii) low-level feature enhancement module (LFE). HFE utilizes attention mechanism to selectively aggregate the optimal feature information in high- and low-levels, enhancing the ability of high-level features to reconstruct accurate details. LFE aims to use global semantic information of high-level features to adaptively guide feature learning of bottom networks, so as to enhance the semantic consistency of high- and low-level features. We integrate HFE and LFE into a typical encoder-decoder network, and propose a novel medical image segmentation framework (HLF-Net). On two challenging datasets of skin lesion segmentation and spleen segmentation, we prove that the proposed modules and network can improve the performance considerably.