Fast and automatic segmentation of pulmonary lobes from chest CT using a progressive dense V-network

Abstract

ABSTRACT Automatic, reliable lobe segmentation is crucial to the diagnosis, assessment, and quantification of pulmonary diseases. Existing pulmonary lobe segmentation techniques are prohibitively slow, undesirably rely on prior (airway/vessel) segmentation, and/or require user interactions for optimal results. We introduce a reliable, fast, and fully automated lung lobe segmentation method based on a Progressive Dense V-Network (PDV-Net). The proposed method can segment lung lobes in one forward pass of the network, with an average runtime of 2 seconds using a single Nvidia Titan XP GPU. An extensive robustness analysis of our method demonstrates reliable lobe segmentation of both healthy and pathological lungs in CT images acquired by scanners from different vendors, across various CT scan protocols and acquisition parameters.