School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.
Comput Biol Med. 2024 Jul;177:108674. doi: 10.1016/j.compbiomed.2024.108674. Epub 2024 May 27.
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.
准确的肺结节分割对于后续的病理分析和诊断至关重要。然而，目前的 U-Net 架构通常依赖于简单的跳过连接方案，导致具有不同语义信息的特征图融合，这可能对分割模型产生负面影响。针对这一挑战，本研究引入了一种专门用于肺结节分割的新型 U 形模型。所提出的模型结合了 U-Net 骨干、语义聚合特征金字塔模块和反向注意力模块等功能。语义聚合模块结合了语义信息和多尺度特征，解决了编码器和解码器之间的语义差距。反向注意力模块通过从侧输出特征中擦除当前预测的显著区域，探索缺失的目标部分并捕获复杂的细节。该模型使用 LIDC-IDRI 数据集进行评估。实验结果表明，所提出的方法的骰子相似系数为 89.11%，灵敏度为 90.73%，全面优于最先进的方法。
