Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks.

ABSTRACT: This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT'09 public dataset. We compared our method with other state-of-the-art airway

SUBMITTER: Garcia-Uceda A 

PROVIDER: S-EPMC8346579 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature