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Medially constrained deformable modeling for segmentation ofbranching medial structures: Application to aortic valve segmentation andmorphometry

机译：医学约束的可变形模型用于分割分支内侧结构：在主动脉瓣分割和应用中的应用形态学

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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 of0.41 ± 0.10 mm (approximately one voxel) in 22 3DE images of normalaortic valves at systole. Deformable medial modeling is additionallydemonstrated on pathological cases, including aortic stenosis, Marfan syndrome,and bicuspid aortic valve disease. This study demonstrates a promising approachfor quantitative 3DE analysis of aortic valve morphology.
机译：具有中间轴表示的可变形建模是分割和参数化描述医学图像中解剖结构形状的有用手段。连续内侧表示（cm-rep）是可变形内侧建模的“骨架优先”方法，该方法可显式参数化对象的中间轴并通过算法得出对象的边界。尽管cm-rep已被有效地用于分割和建模具有非分支内侧拓扑的许多解剖结构，但是该框架很难应用于具有分支内侧几何形状的对象，因为内侧轴上的分支曲线很难参数化。在这项工作中，我们演示了一种新的“边界优先”可变形内侧建模范例的首次临床应用，其中明确描述了对象的边界，并对边界几何形状施加了约束，以在模型变形期间保留内侧轴的分支配置。该“边界优先”框架可用于对3D超声心动图图像中的主动脉瓣装置进行分割和形态分析。相对于手动跟踪，使用可变形内侧模型进行的分割实现了平均边界误差为在正常的22张3DE图像中为0.41±0.10 mm（大约一个体素）收缩期主动脉瓣。另外，还需要可变形的中间模型在包括主动脉瓣狭窄，马凡氏综合征，和二尖瓣主动脉瓣疾病。这项研究表明了一种有前途的方法用于定量3DE分析主动脉瓣形态。

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