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Proceedings Paper

Aorta and pulmonary artery segmentation using optimal surface graph cuts in non-contrast CT
Author(s): Zahra Sedghi Gamechi; Andres M. Arias-Lorza; Jesper Holst Pedersen; Marleen de Bruijne
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Paper Abstract

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.

Paper Details

Date Published: 2 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742D (2 March 2018); doi: 10.1117/12.2293748
Show Author Affiliations
Zahra Sedghi Gamechi, Erasmus MC (Netherlands)
Andres M. Arias-Lorza, Erasmus MC (Netherlands)
Jesper Holst Pedersen, Copenhagen Univ. Hospital (Denmark)
Marleen de Bruijne, Erasmus MC (Denmark)
Univ. of Copenhagen (Denmark)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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