Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database
This work addresses liver tumor segmentation for hepatocellular carcinoma diagnosis and surgical planning, representing an incremental improvement with novel attention mechanisms.
The paper tackles liver tumor segmentation from CT volumes by proposing a Semantic Feature Attention Network (SFAN) that uses attention modules to integrate low-level and high-level features, achieving state-of-the-art performance on the LiTS database and outperforming other methods on a large-scale clinical database with 912 CT volumes.
Liver tumor segmentation plays an important role in hepatocellular carcinoma diagnosis and surgical planning. In this paper, we propose a novel Semantic Feature Attention Network (SFAN) for liver tumor segmentation from Computed Tomography (CT) volumes, which exploits the impact of both low-level and high-level features. In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information. Furthermore, a Global Context Attention (GCA) module is proposed to effectively fuse the multi-level features with the guidance of global context. Our experiments are based on 2 challenging databases, the public Liver Tumor Segmentation (LiTS) Challenge database and a large-scale in-house clinical database with 912 CT volumes. Experimental results show that our proposed framework can not only achieve the state-of-the-art performance with the Dice per case on liver tumor segmentation in LiTS database, but also outperform some widely used segmentation algorithms in the large-scale clinical database.