Pulmonary Lobe Segmentation Using A Sequence of Convolutional Neural Networks For Marginal Learning

Abstract

Segmentation of the pulmonary lobes in computed tomography images is an important precursor for characterizing and quantifying disease patterns, regional functional analysis, and determining treatment interventions. With the increasing resolution and quantity of scans produced in the clinic automatic and reliable lobar segmentation methods are essential for efficient workflows. In this work, a deep learning framework is proposed that utilizes convolutional neural networks for segmentation of fissures and lobes in computed tomography images. A novel pipeline is proposed that consists of a series of 3D convolutional neural networks to marginally learn the lobe segmentation. The method was evaluated extensively on a dataset of 1076 CT images from the COPDGene clinical trial, consisting of scans acquired multiple institutions using various scanners. Overall the method achieved median Dice coefficient of 0.993 and a median average symmetric surface distance of 0.138 mm across all lobes. The results show the method is robust to different inspiration levels, pathologies, and image quality.