title: Medially constrained deformable modeling for segmentation of branching medial structures: Application to aortic valve segmentation and morphometry.
abstract: 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.
authors.affiliation: Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States . Electronic address: alison.pouch@gmail.com.
authors.forename: Alison M
authors.initials: AM
authors.lastname: Pouch
authors.affiliation: Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Sijie
authors.initials: S
authors.lastname: Tian
authors.affiliation: Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Manabu
authors.initials: M
authors.lastname: Takebe
authors.affiliation: Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Jiefu
authors.initials: J
authors.lastname: Yuan
authors.affiliation: Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Robert
authors.initials: R
authors.lastname: Gorman
authors.affiliation: Deparment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, United States.
authors.forename: Albert T
authors.initials: AT
authors.lastname: Cheung
authors.affiliation: IBM Almaden Research Center, San Jose, CA, United States.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.affiliation: Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Jackson
authors.affiliation: Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Joseph H
authors.initials: JH
authors.lastname: Gorman
authors.affiliation: Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Gorman
authors.affiliation: Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Yushkevich
mesh_terms: D000465:Algorithms; D001021:Aortic Valve / Q000000981:diagnostic imaging*; D001024:Aortic Valve Stenosis / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: 3D echocardiography; Aortic valve; Deformable modeling; Image segmentation; Medial axis representation