Trachea Segmentation¶

Since changes in the appearance and behaviour during breathing of the trachea can be indicators of disease, CIRRUS Lung incorporates a trachea segmentation. The trachea is automatically extracted starting by searching for a central dark circular region as a starting point. From this starting point, the trachea is grown using region growing with an automatically determined optimal threshold. The result of the region growing will include both the trachea and main bronchi. To determine the exact location of the trachea, the carina is automatically detected by growing a wavefront from the starting seed point and determining where the wavefront splits. In CIRRUS Lung, the segmentation of the trachea can be inspected simultaneously with the lung segmentation as an overlay. In the rare case of a erroneous seed point location, CIRRUS Lung allows the user to set a seed point in the trachea after which the segmentation will be rerun. 

Technical publications about trachea segmentation algorithms included in CIRRUS Lung¶

E. van Rikxoort, W. Baggerman and B. van Ginneken. "Automatic segmentation of the airway tree from thoracic CT scans using a multi-threshold approach", in: The Second International Workshop on Pulmonary Image Analysis, 2009, pages 341-349. Abstract/PDF

E.M. van Rikxoort, B. de Hoop, M.A. Viergever, M. Prokop and B. van Ginneken. "Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection", *Medical Physics *2009;36:2934-2947. Abstract/PDF DOI PMID

B. van Ginneken, W. Baggerman and E.M. van Rikxoort. "Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans", in: Medical Image Computing and Computer-Assisted Intervention, volume 5241 of Lecture Notes in Computer Science, 2008, pages 219-226. Abstract/PDF DOI PMID

I. Sluimer, M. Prokop and B. van Ginneken. "Towards automated segmentation of the pathological lung in CT",IEEE Transactions on Medical Imaging 2005;24:1025-1038. Abstract/PDF DOI PMID