Deformable modeling with medial axis representation is a useful means of
segmenting and parametrically describing the shape of anatomical structures in
medical images. Continuous medial representation (cm-rep) is a
“skeleton-first” approach to deformable medial modeling that
explicitly parameterizes an object’s medial axis and derives the
object’s boundary algorithmically. Although cm-rep has effectively been
used to segment and model a number of anatomical structures with non-branching
medial topologies, the framework is challenging to apply to objects with
branching medial geometries since branch curves in the medial axis are difficult
to parameterize. In this work, we demonstrate the first clinical application of
a new “boundary-first” deformable medial modeling paradigm,
wherein an object’s boundary is explicitly described and constraints are
imposed on boundary geometry to preserve the branching configuration of the
medial axis during model deformation. This “boundary-first”
framework is leveraged to segment and morphologically analyze the aortic valve
apparatus in 3D echocardiographic images. Relative to manual tracing,
segmentation with deformable medial modeling achieves a mean boundary error of
0.41 ± 0.10 mm (approximately one voxel) in 22 3DE images of normal
aortic valves at systole. Deformable medial modeling is additionally
demonstrated on pathological cases, including aortic stenosis, Marfan syndrome,
and bicuspid aortic valve disease. This study demonstrates a promising approach
for quantitative 3DE analysis of aortic valve morphology.
Abstract3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.
Background
Degenerative mitral valve disease is associated with variable and complex defects in valve morphology. Three-dimensional echocardiography (3DE) has shown promise in aiding preoperative planning for patients with this disease but to date has not been as transformative as initially predicted. The clinical usefulness of 3DE has been limited by the laborious methods currently required to extract quantitative data from the images.
Methods
To maximize the utility of 3DE for preoperative valve evaluation, this work describes an automated 3DE image analysis method for generating models of the mitral valve that are well suited for both qualitative and quantitative assessment. The method is unique in that it captures detailed alterations in mitral leaflet and annular morphology and produces image-derived models with locally varying leaflet thickness. The method is evaluated on midsystolic transesophageal 3DE images acquired from 22 subjects with myxomatous degeneration and from 22 subjects with normal mitral valve morphology.
Results
Relative to manual image analysis, the automated method accurately represents both normal and complex leaflet geometries with a mean boundary displacement error on the order of one image voxel. A detailed quantitative analysis of the valves is presented and reveals statistically significant differences between normal and myxomatous valves with respect to numerous aspects of annular and leaflet geometry.
Conclusions
This work demonstrates a successful methodology for the relatively rapid quantitative description of the complex mitral valve distortions associated with myxomatous degeneration. The methodology has the potential to significantly improve surgical planning for patients with complex mitral valve disease.
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