  Mitral valve regurgitation is the most common valvular disease, affecting 10%
of the population over 75 years old. Left untreated, patients with mitral valve
regurgitation can suffer declining cardiac health until cardiac failure and
death. Mitral valve repair is generally preferred over valve replacement.
However, there is a direct correlation between the volume of cases performed
and surgical outcomes, therefore there is a demand for the ability of surgeons
to practice repairs on patient specific models in advance of surgery. This work
demonstrates a semi-automated segmentation method to enable fast and accurate
modelling of the mitral valve that captures patient-specific valve geometry.
This modelling approach utilizes 3D active contours in a user-in-the-loop
system which segments first the atrial blood pool, then the mitral leaflets. In
a group of 15 mitral valve repair patients, valve segmentation and modelling
attains an overall accuracy (mean absolute surface distance) of 1.40+-0.26 mm,
and an accuracy of 1.01+-0.13 mm when only comparing the extracted leaflet
surface proximal to the ultrasound probe. Thus this image-based segmentation
tool has the potential to improve the workflow for extracting patient-specific
mitral valve geometry for 3D modelling of the valve.
