HAUNet-3D: a Novel Hierarchical Attention 3D UNet for Lung Nodule Segmentation

UNet and its extended versions are the most used networks in the lung nodule segmentation from CT images. However, current UNet-like methods still suffer from some problems: 1) The heterogeneity of lung nodules affect the segmentation performance; 2) the mixture of lung nodules and their surrounding tissues in the CT image increases the segmentation difficulty. To address these issues, we propose a novel hierarchical attention 3D UNet named HAUNet-3D. It introduces the attention mechanism at multiple scales and organizes them in a bottom-up hierarchical connection way. Such a proposition could better capture features with various sizes and guide the fusion of features from adjacent attention outputs without losing the advantages of 3D UNet. In experiment, our method has been extensively evaluated on the public LUNA16 dataset. It achieves competitive segmentation performance on dice similarity coefficient of 83.34% and average surface distance of 0.28 mm. More importantly, our method is proven to be more robust to the heterogeneous types of lung nodules and shows better segmentation performance on small lung nodules.