Learning multi-view and centerline topology connectivity information for pulmonary artery–vein separation

Abstract

Background:Automatic pulmonary artery–vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery–vein separation. Methods:A novel automatic method for artery–vein separation in CT images is presented in this work. Specifically, a multi-scale information aggregated network (MSIA-Net) including multi-scale fusion blocks and deep supervision, is proposed to learn the features of artery–vein and aggregate additional semantic information, respectively. The proposed method integrates nine MSIA-Net models for artery–vein separation, vessel segmentation, and centerline separation tasks along with axial, coronal, and sagittal multi-view slices. First, the preliminary artery–vein separation results are obtained by the proposed multi-view fusion strategy (MVFS). Then, centerline correction algorithm (CCA) is used to correct the preliminary results of artery–vein separation by the centerline separation results. Finally, the vessel segmentation results are utilized to reconstruct the artery–vein morphology. In addition, weighted cross-entropy and dice loss are employed to solve the class imbalance problem. Results:We constructed 50 manually labeled contrast-enhanced computed CT scans for five-fold cross-validation, and experimental results demonstrated that our method achieves superior segmentation performance of 97.7%, 85.1%, and 84.9% on ACC, Pre, and DSC, respectively. Additionally, a series of ablation studies demonstrate the effectiveness of the proposed components. Conclusion:The proposed method can effectively solve the problem of insufficient vascular connectivity and correct the spatial inconsistency of artery–vein.