Tracheal stenosis is a narrowing of the trachea that impedes normal breathing. Tracheotomy is one solution, but
subjects patients to intubation. An alternative technique employs tracheal stents, which are tubular structures
that push the walls of the stenotic areas to their original location. They are implanted with endoscopes, therefore
reducing the surgical risk to the patient. Stents can also be used in tracheal reconstruction to aid the recovery
of reconstructed areas. Correct preoperative stent length and diameter specification is crucial to successful
treatment, otherwise stents might not cover the stenotic area nor push the walls as required. The level of
stenosis is usually measured from inside the trachea, either with endoscopes or with image processing techniques
that, eg compute the distance from the centre line to the walls of the trachea. These methods are not suited
for the prediction of stent sizes because they can not trivially estimate the healthy calibre of the trachea at the
stenotic region. We propose an automatic method that enables the estimation of stent dimensions with statistical
shape models of the trachea. An average trachea obtained from a training set of CT scans of healthy tracheas
is placed in a CT image of a diseased person. The shape deforms according to the statistical model to match
the walls of the trachea, except at stenotic areas. Since the deformed shape gives an estimation of the healthy
trachea, it is possible to predict the size and diameter of the stent to be implanted in that specific subject.
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