Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow.

ABSTRACT:

Purpose

To present and validate a fully automated airway detection method at low-dose CT in patients with chronic obstructive pulmonary disease (COPD).

Materials and methods

In this retrospective study, deep learning (DL) and freeze-and-grow (FG) methods were optimized and applied to automatically detect airways at low-dose CT. Four data sets were used: two data sets consisting of matching standard- and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II (2014-2017) cohort (n = 2 × 236; mean age ± SD, 70 years ± 9; 123 women); one data set consisting of low-dose CT scans from the COPDGene phase III (2018-2020) cohort (n = 335; mean age ± SD, 73 years ± 8; 173 women); and one data set consisting of low-dose, anonymized CT scans from the 2

SUBMITTER: Nadeem SA 

PROVIDER: S-EPMC9806731 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature