Fully Automated Lung Volume Assessment from MRI in a Population-based Child Cohort Study

In this work, a framework for fully automated lung extraction from magnetic resonance imaging (MRI) inspiratory data that have been acquired within a on-going epidemiological child cohort study is presented. The method's main steps are intensity inhomogeneity correction, denoising, clustering, airway extraction and lung region refinement. The presented approach produces highly accurate results (Dice coefficients >= 95%), when compared to semi-automatically obtained masks, and has potential to be applied to the whole study data.