Loop Structure-Aware Learning for Fully Automated Pulmonary Fissure Completeness Assessment

Pulmonary fissures are anatomical biomarkers used to evaluate the severity of chronic obstructive pulmonary disease. The completeness of the fissures is significantly associated with this disease. This work proposes a new fully automated fissure completeness assessment framework on the basis of deeply learned pulmonary fissure and lobe segmentation. This framework consists of automatic loop structure-aware learning for joint fissure-lobe segmentation and fissure integrity calculation. Specifically, the loop segmentation performs (1) attention-gated U-transformers for fissure segmentation, (2) 3-D U-Net to extract pulmonary lobes on the basis of the segmented fissures, and (3) attention-gated U-transformers to refine the segmented fissures using the extracted lobes. Based on accurately segmented fissures and interlobar boundaries, we develop a new fissure completeness assessment method. We evaluated our framework on 54 CT volumes, with the experimental results showing that our loop segmentation methods can extract fissures and lobar boundaries more accurately than state-of-the-art methods for the fissure completeness calculation. Particularly, our fissure completeness computing method provides chronic obstructive pulmonary disease with a promising assessment way.