Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT

TY - GEN

T1 - Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT

AU - Sedghi Gamechi, Zahra

AU - Arias-Lorza, Andres M.

AU - Pedersen, Jesper Holst

AU - De Bruijne, Marleen

PY - 2018

Y1 - 2018

N2 - Accurate measurements of the size and shape of the aorta and pulmonary arteries are important as risk factors for cardiovascular diseases, and for Chronicle Obstacle Pulmonary Disease (COPD).1 The aim of this paper is to propose an automated method for segmenting the aorta and pulmonary arteries in low-dose non-ECGgated non-contrast CT scans. Low contrast and the high noise level make the automatic segmentation in such images a challenging task. In the proposed method, first, a minimum cost path tracking algorithm traces the centerline between user-defined seed points. The cost function is based on a multi-directional medialness filter and a lumen intensity similarity metric. The vessel radius is also estimated from the medialness filter. The extracted centerlines are then smoothed and dilated non-uniformly according to the extracted local vessel radius and subsequently used as initialization for a graph-cut segmentation. The algorithm is evaluated on 225 low-dose non-ECG-gated non-contrast CT scans from a lung cancer screening trial. Quantitatively analyzing 25 scans with full manual annotations, we obtain a dice overlap of 0.94±0.01 for the aorta and 0.92±0.01 for pulmonary arteries. Qualitative validation by visual inspection on 200 scans shows successful segmentation in 93% of all cases for the aorta and 94% for pulmonary arteries.

AB - Accurate measurements of the size and shape of the aorta and pulmonary arteries are important as risk factors for cardiovascular diseases, and for Chronicle Obstacle Pulmonary Disease (COPD).1 The aim of this paper is to propose an automated method for segmenting the aorta and pulmonary arteries in low-dose non-ECGgated non-contrast CT scans. Low contrast and the high noise level make the automatic segmentation in such images a challenging task. In the proposed method, first, a minimum cost path tracking algorithm traces the centerline between user-defined seed points. The cost function is based on a multi-directional medialness filter and a lumen intensity similarity metric. The vessel radius is also estimated from the medialness filter. The extracted centerlines are then smoothed and dilated non-uniformly according to the extracted local vessel radius and subsequently used as initialization for a graph-cut segmentation. The algorithm is evaluated on 225 low-dose non-ECG-gated non-contrast CT scans from a lung cancer screening trial. Quantitatively analyzing 25 scans with full manual annotations, we obtain a dice overlap of 0.94±0.01 for the aorta and 0.92±0.01 for pulmonary arteries. Qualitative validation by visual inspection on 200 scans shows successful segmentation in 93% of all cases for the aorta and 94% for pulmonary arteries.

KW - aorta

KW - centerline extraction

KW - chest CT

KW - COPD

KW - graph cut

KW - pulmonary artery

KW - segmentation

UR - http://www.scopus.com/inward/record.url?scp=85047351716&partnerID=8YFLogxK

U2 - 10.1117/12.2293748

DO - 10.1117/12.2293748

M3 - Article in proceedings

AN - SCOPUS:85047351716

T3 - Proceedings of SPIE International Symposium on Medical Imaging

BT - Medical Imaging 2018

PB - SPIE - International Society for Optical Engineering

T2 - SPIE Medical Imaging 2018

Y2 - 10 February 2018 through 15 February 2018

ER -