3D patch-based CNN for fissure segmentation on CT images to quantitatively assess fissure integrity and evaluate emphysema patients for endobronchial valve treatment

Abstract

Complete pulmonary fissures are assessed on computed tomography (CT) and are required for emphysema patients to be successfully treated with endobronchial valve (EBV) therapy. We propose a deep learning (DL) pipeline that uses a patch-based approach to quantitatively assess fissure completeness on CT and evaluate it in a clinical trial cohort. From the EBV for emphysema palliation trial (VENT), 130 CT scans were used in this study. The DL model utilizes nnU-Net as a backbone for the automatic pre- and post-processing of CT images and configuration of a 3D U-Net to segment patches of fissure and non-fissure. Five-fold cross validation is applied for training and inferences are obtained using a sliding window approach. Average symmetric surface distance (ASSD) and surface dice coefficient (SDC) at a threshold of 2mm evaluates segmentation performance. A fissure integrity score (FIS) is calculated as the percentage of complete fissure voxels along the surface of the assumed interlobar region using pulmonary lobar segmentations. A predicted-FIS (p-FIS) is quantified from the CNN output and is compared to the reference-FIS (r-FIS) as complete (FIS≥90%), partial (10%≤ FIS< 90%) or absent (FIS< 10%). A mean(±SD) SDC of 0.95(±0.037) is achieved for the left oblique fissure (LOF); 0.84(±0.144) for the right horizontal fissure (RHF), and 0.94(±0.098) for the right oblique fissure (ROF). Concordance rate of p-FIS and r-FIS is 86.4%, 88.6%, and 86.4% for the LOF, RHF, and ROF, respectively. A DL pipeline using a patch-based approach has potential to segment interlobar fissures from CT to quantitatively assess fissure completeness.