SaraNet: Semantic aggregation reverse attention network for pulmonary nodule segmentation

Abstract

Accurate segmentation of pulmonary nodule is essential for subsequent pathological analysis and diagnosis. However, current U-Net architectures often rely on a simple skip connection scheme, leading to the fusion of feature maps with different semantic information, which can have a negative impact on the segmentation model. In response to this challenge, this study introduces a novel U-shaped model specifically designed for pulmonary nodule segmentation. The proposed model incorporates features such as the U-Net backbone, semantic aggregation feature pyramid module, and reverse attention module. The semantic aggregation module combines semantic information with multi-scale features, addressing the semantic gap between the encoder and decoder. The reverse attention module explores missing object parts and captures intricate details by erasing the currently predicted salient regions from side-output features. The proposed model is evaluated using the LIDC-IDRI dataset. Experimental results reveal that the proposed method achieves a dice similarity coefficient of 89.11%and a sensitivity of 90.73 %, outperforming state-of-the-art approaches comprehensively.