Lung segmentation and airway tree matching - application to aeration quantification in CT images of subjects with ARDS

Abstract

Acute Respiratory Distress Syndrome (ARDS) is a life threatening disease presenting a high mortality of about 40% in intensive care units. lt is the consequence of different pulmonary aggressions generating hypoxemia and pulmonary edema, which are radiologically expressed as infiltrations observable as opaque regions in the lung. The treatment of ARDS requires mechanical ventilation, which may deteriorate the state of the patient if the ventilation parameters, namely volume and pressure, are not correctly adjusted. To adjust the parameter settings to each individual case, lung aeration - in response to ventilation - needs to be assessed. This assessment can be done using computed tomography (CT) images. However, it requires the segmentation of the hung-parenchymal tissue, which is a challenging task in ARDS image; due the opacities that hinder the image contrast. In this thesis we aim to provide the required tools for the experts to analyze the aeration in the images acquired within an ARDS project using an animal model