Margarete Ortner

TL;DR: A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

TL;DR: A generic and automated 3D airway segmentation approach able to deal with a large spectrum of MSCT protocols by exploiting a combined morphologicalaggregative methodology is developed.

TL;DR: A fullyautomated airway shape assessment approach based on the 3D segmentation of the airway lumen from MSCT data, evaluated on a MSCT database including twelve patients with severe or moderate persistent asthma, or severe COPD, by analyzing segmental and subsegmental bronchi of the right lung.

TL;DR: The volumetric segmentation of the airway wall from CT data is addressed by exploiting a patient-specific surface active model and allows the quantification of the tissue thickness based on a locally-defined measure sensitive to even small surface irregularities.

TL;DR: In this article, a 3D automated approach for airway wall segmentation and quantification in MSCT based on a patient-specific deformable model is developed, which is explicitly defined as a triangular surface mesh at the level of the airway lumen segmented from the MSCT data.