3D pulmonary vessel segmentation based on improved residual attention u-net

Abstract

Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases. The accuracy of segmentation is suffering from the complex vascular structure. In this paper, an Improved Residual Attention U-Net (IRAU-Net) aiming to segment pulmonary vessel in 3D is proposed. To extract more vessel structure information, the Squeeze and Excitation (SE) block is embedded in the down sampling stage. And in the up sampling stage, the global attention module (GAM) is used to capture target features in both high and low levels. These two stages are connected by Atrous Spatial Pyramid Pooling (ASPP) which can sample in various receptive fields with a low computational cost. By the evaluation experiment, the better performance of IRAU-Net on the segmentation of terminal vessel is indicated. It is expected to provide robust support for clinical diagnosis and treatment.

Keywords

• Pulmonary vessel segmentation
• RAU-Net
• Squeeze and excitation
• Atrous spatial pyramid pooling
• Deep learning