Assessment and stenting of tracheal stenosis using deformable shape models

• Published in: Medical image analysis Vol. 15; no. 2; pp. 250 - 266
• Main Authors: Pinho, Rômulo, Tournoy, Kurt G., Sijbers, Jan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2011
• Subjects: Active contour model; Algorithms; Computer Simulation; Humans; Models, Biological; Pattern Recognition, Automated - methods; Prognosis; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; Statistical shape model; Stents; Subtraction Technique; Tomography, X-Ray Computed - methods; Tracheal stenosis; Tracheal Stenosis - diagnostic imaging; Tracheal Stenosis - surgery; Tracheal stenting; Treatment Outcome; Statistical shape model; Active contour model; Tracheal stenosis; Tracheal stenting
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2010.12.001

The average shape of an active shape model of healthy tracheas is first rigidly aligned with the patient’s narrowed trachea in the CT scan. The new Fixed Landmarks method registers the shape model to the narrowed trachea so as to estimate the patient’s trachea without stenosis. The narrowed trachea is segmented from the CT scan with an active contour model, using the estimated healthy trachea as the starting point. The cross-sectional area profiles of the healthy and the narrowed versions of the trachea are obtained. The profiles determine the parameters of the stenosis (start, end, degree of narrowing) and the dimensions (length, diameter) and deployment location of the suggested stent. [Display omitted] ► Automatic assessment and stenting of tracheal stenosis. ► Based on deformable shape models. ► Estimation of the patient’s trachea without stenosis. ► New method for statistical shape model registration. ► Accurate and fast enough to be used in the clinical setting. This work presents a decision support system for the assessment of tracheal stenosis. In the proposed method, a statistical shape model of healthy tracheas is registered to a 3D CT image of a patient with tracheal stenosis. The registration yields an estimation of the shape of the patient’s trachea as if stenosis was not present. From this point, the extent and the severity of the stenosis is assessed and stent parameters are obtained automatically. The method was extensively evaluated on simulation as well on real data and the results showed that it is accurate and fast enough to be used in the clinical setting.