COPD Classification in CT Images Using a 3D Convolutional Neural Network

Type: MA thesis

Status: finished

Date: January 1, 2019 - August 23, 2019

Supervisors: Sulaiman Vesal, Andreas Maier, Tino Haderlein

Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a difficult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing methods that can automatically classify COPD versus healthy patients is of great interest. In this thesis we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also investigate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.