A Convolutional Neural Network Approach to Automated Lung Bounding Box Estimation from Computed Tomography Scans

摘要

In this work, a convolutional neural network (CNN) based method for automated lung boundary estimation from computed tomography (CT) scans is presented and validated. The CNN model was trained to regress the locations of the superior and inferior borders of the lungs from multiple tissue-specific 2D projections of thoracic CT images. The model utilized a DenseNet architecture and was trained and evaluated on CT images from the COPDGene study. The median (95th percentile) localization error was 2.51 (11.18) for the inferior border and 1.52 (7.21) for the superior border of the lungs.