Medially constrained deformable modeling for segmentation of branching medial structures: Application to aortic valve segmentation and morphometry.
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.
Published In/Presented At
Pouch, A. M., Tian, S., Takebe, M., Yuan, J., Gorman, R., Jr, Cheung, A. T., Wang, H., Jackson, B. M., Gorman, J. H., 3rd, Gorman, R. C., & Yushkevich, P. A. (2015). Medially constrained deformable modeling for segmentation of branching medial structures: Application to aortic valve segmentation and morphometry. Medical image analysis, 26(1), 217–231. https://doi.org/10.1016/j.media.2015.09.003
Medicine and Health Sciences
Department of Medicine, Cardiology Division