Pulmonary nodule image segmentation network based on two-stream feature fusion
In order to enhance the segmentation accuracy of pulmonary nodules,this study proposes a dual-backbone network feature extraction method named CSF-UNet.Two backbone networks with different emphases are used to extract image features in parallel.ConvNeXt is employed to capture local features,while Swin Transformer is utilized to extract global features,so as to enhance the feature extraction capabilities of the model.An adaptive large kernel fusion module is introduced to integrate features of different scales effectively.By concatenating two large kernel convolutions,a larger receptive field and a dynamic selection mechanism are achieved to highlight important spatial regions.The ECA(efficient channel attention)and dense connections are integrated into SPPF(spatial pyramid pooling fusion),and an ESPP module is proposed to further exploit the high-level semantic information extracted by the dual-backbone networks,so as to make the network focus on critical feature channels.Experimental results on the LIDC dataset demonstrate that in terms of the three indicators the proposed model outperforms the baseline model UNet and other recent segmentation networks developed for this dataset and proposed by other research teams.Ultimately,the CSF-UNet model achieves IoU(intersection over union)of 78.1%,DSC(dice similarity coefficient)of 87.71%,sensitivity of 87.19%and precision of 88.23%.These results indicate that the proposed model exhibits robust performance in pulmonary nodule segmentation,holding significant clinical implications and application value for the diagnosis of early-stage pulmonary nodule.
万方数据
维普
