Pouch Alison M, Tian Sijie, Takebe Manabu, Yuan Jiefu, Gorman Robert, Cheung Albert T, Wang Hongzhi, Jackson Benjamin M, Gorman Joseph H, Gorman Robert C, Yushkevich Paul A
Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States .
Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States.
Med Image Anal. 2015 Dec;26(1):217-31. doi: 10.1016/j.media.2015.09.003. Epub 2015 Sep 28.
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.
基于中轴线表示的可变形建模是医学图像中解剖结构形状分割和参数化描述的一种有用方法。连续中轴线表示(cm-rep)是一种用于可变形中轴线建模的“先骨架”方法,它明确地对物体的中轴线进行参数化,并通过算法推导物体的边界。尽管cm-rep已有效地用于分割和建模许多具有非分支中轴线拓扑结构的解剖结构,但由于中轴线中的分支曲线难以参数化,该框架应用于具有分支中轴线几何形状的物体时具有挑战性。在这项工作中,我们展示了一种新的“先边界”可变形中轴线建模范式的首次临床应用,其中明确描述了物体的边界,并对边界几何施加约束,以在模型变形期间保留中轴线的分支配置。利用这种“先边界”框架对三维超声心动图图像中的主动脉瓣装置进行分割和形态分析。相对于手动追踪,在22幅正常主动脉瓣收缩期的三维超声心动图图像中,使用可变形中轴线建模进行分割的平均边界误差为0.41±0.10毫米(约一个体素)。还在包括主动脉狭窄、马凡综合征和二叶式主动脉瓣疾病在内的病理病例中展示了可变形中轴线建模。这项研究展示了一种用于主动脉瓣形态定量三维超声心动图分析的有前景的方法。