SCLMnet: A dual-branch guided network for lung and lung lobe segmentation

Abstract

Lung and lung lobe segmentation are two crucial techniques for lung imaging analysis that interact in clinical settings. Lung segmentation assists physicians in comparing different images to select the most appropriate surgical plan, while lobe segmentation provides precise anatomical information to help plan surgical procedures. However, inaccurate lung segmentation edges, mis-segmented lobe boundaries, and tiny targets pose challenges. Therefore, we propose a dual-branch guided convolutional neural network, SCLMnet, for lung and lung lobe segmentation. To completely leverage the semantic information of feature maps, the first branch adds a spatial linkage module (SLM) to focus on low-level features at different spatial levels, highlighting feature representations of lung edges and lung lobe boundaries. A channel linkage module (CLM) is added by matrix inner product to model channel relations, emphasizing the relevance and similarity of feature maps and capturing the interdependency of high-level feature channels to highlight feature representations of the entire lung lobe region. Transmodal synaptic linkage (TSL) and multi-scale fusion strategy guide the feature information of the CLM and SLM and the deep features extracted by the second branch ResUNet to jointly explore useful information in chest computer tomography (CT) images. To evaluate the performance of the state-of-the-art model, we use three publicly available datasets: LUNA16, COVID-19-CT-Seg, and VESSEL12. Compared to the existing methods, SCLMnet achieves average Dice scores of 92.17%, 97.80%, and 99.12%, respectively, demonstrating remarkable performance, which suggests that lung and lung lobe segmentation using CT images with SCLMnet can play an essential role in clinical research.