Medially constrained deformable modeling for segmentation of branching medial structures: Application to aortic valve segmentation and morphometry
被引:24
作者:
Pouch, Alison M.
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机构:
Univ Penn, Dept Surg, Philadelphia, PA 19104 USA
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Pouch, Alison M.
[1
,2
]
Tian, Sijie
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h-index: 0
机构:
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Tian, Sijie
[2
]
Takebe, Manabu
论文数: 0引用数: 0
h-index: 0
机构:
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Takebe, Manabu
[2
]
Yuan, Jiefu
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h-index: 0
机构:
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Yuan, Jiefu
[2
]
Gorman, Robert, Jr.
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机构:
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Gorman, Robert, Jr.
[2
]
Cheung, Albert T.
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Stanford Univ, Dept Anesthesiol Perioperat & Pain Med, Stanford, CA 94305 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Cheung, Albert T.
[3
]
Wang, Hongzhi
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机构:
IBM Almaden Res Ctr, San Jose, CA USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Wang, Hongzhi
[4
]
Jackson, Benjamin M.
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Univ Penn, Dept Surg, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Jackson, Benjamin M.
[1
]
Gorman, Joseph H., III
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机构:
Univ Penn, Dept Surg, Philadelphia, PA 19104 USA
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Gorman, Joseph H., III
[1
,2
]
Gorman, Robert C.
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Univ Penn, Dept Surg, Philadelphia, PA 19104 USA
Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Gorman, Robert C.
[1
,2
]
Yushkevich, Paul A.
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机构:
Univ Penn, Dept Radiol, Philadelphia, PA 19104 USAUniv Penn, Dept Surg, Philadelphia, PA 19104 USA
Yushkevich, Paul A.
[5
]
机构:
[1] Univ Penn, Dept Surg, Philadelphia, PA 19104 USA
[2] Univ Penn, Gorman Cardiovasc Res Grp, Philadelphia, PA 19104 USA
[3] Stanford Univ, Dept Anesthesiol Perioperat & Pain Med, Stanford, CA 94305 USA
[4] IBM Almaden Res Ctr, San Jose, CA USA
[5] Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA
Image segmentation;
Deformable modeling;
Medial axis representation;
Aortic valve;
3D echocardiography;
LEVEL SET METHOD;
SHAPE-ANALYSIS;
REGISTRATION;
REPRESENTATION;
D O I:
10.1016/j.media.2015.09.003
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
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. (C) 2015 Elsevier B.V. All rights reserved.