MAU-Net: Multiple Attention 3D U-Net for Lung Cancer Segmentation on CT Images

Abstract

Accurate segmentation of lung cancer from computed tomography (CT) is of great significance to constructing an automatic diagnosis system for lung cancer. This paper presents multiple attention 3D U-Net (MAU-Net), a novel deep learning-based architecture for lung cancer segmentation from CT images. In particular, we first apply a dual attention module at the bottleneck of the U-Net that models the semantic interdependencies in spatial and channel dimensions, respectively. A novel multiple attention gate module is then proposed to adaptively recalibrate and fuse multiscale features from the dual attention module, the previous decoder feature maps, and the corresponding features from the encoder. Extensive ablation studies on a clinical dataset consisting of 322 CT images demonstrate the effectiveness of our proposed method. Our model achieved an average Dice similarity coefficient, 95% Hausdorff distance and relative absolute volume difference of 0.8667, 13.0036, and 0.1552, respectively.