Goparaju Anupama, Csecs Ibolya, Morris Alan, Kholmovski Evgueni, Marrouche Nassir, Whitaker Ross, Elhabian Shireen
Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
Comprehensive Arrhythmia Research and Management Center, Division of Cardiovascular Medicine, School of Medicine, University of Utah, SLC, UT, USA
Shape Med Imaging (2018). 2018 Sep;11167:14-27. doi: 10.1007/978-3-030-04747-4_2. Epub 2018 Nov 23.
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Recently, the increased availability of high-resolution in vivo images of anatomy has led to the development and distribution of open-source computational tools to model anatomical shapes and their variability within populations with unprecedented detail and statistical power. Nonetheless, there is little work on the evaluation and validation of such tools as related to clinical applications that rely on morphometric quantifications for treatment planning. To address this lack of validation, we systematically assess the outcome of widely used off-the-shelf SSM tools, namely ShapeWorks, SPHARM-PDM, and Deformetrica, in the context of designing closure devices for left atrium appendage (LAA) in atrial fibrillation (AF) patients to prevent stroke, where an incomplete LAA closure may be worse than no closure. This study is motivated by the potential role of SSM in the geometric design of closure devices, which could be informed by population-level statistics, and patient-specific device selection, which is driven by anatomical measurements that could be automated by relating patient-level anatomy to population-level morphometrics. Hence, understanding the consequences of different SSM tools for the final analysis is critical for the careful choice of the tool to be deployed in real clinical scenarios. Results demonstrate that estimated measurements from ShapeWorks model are more consistent compared to models from Deformetrica and SPHARM-PDM. Furthermore, ShapeWorks and Deformetrica shape models capture clinically relevant population-level variability compared to SPHARM-PDM models.
统计形状建模（SSM）作为新一代形态计量学方法，已被证明在生物学和医学的许多领域中对于解剖形状的定量分析很有用。最近，高分辨率体内解剖图像的可用性不断提高，促使开源计算工具的开发和发布，这些工具能够以前所未有的细节和统计能力对解剖形状及其在人群中的变异性进行建模。尽管如此，对于此类依赖形态计量量化进行治疗规划的临床应用相关工具的评估和验证工作却很少。为了解决这种验证不足的问题，我们在为心房颤动（AF）患者设计左心耳（LAA）封堵装置以预防中风的背景下，系统地评估了广泛使用的现成SSM工具，即ShapeWorks、SPHARM-PDM和Deformetrica的结果，因为不完全的LAA封堵可能比不封堵更糟。这项研究的动机在于SSM在封堵装置几何设计中的潜在作用，这可以通过人群水平的统计数据来提供信息，以及患者特异性装置选择，这是由解剖测量驱动的，而解剖测量可以通过将患者水平的解剖结构与人群水平的形态计量学相关联来实现自动化。因此，了解不同SSM工具对最终分析的影响对于在实际临床场景中谨慎选择要部署的工具至关重要。结果表明，与Deformetrica和SPHARM-PDM的模型相比，ShapeWorks模型的估计测量结果更一致。此外，与SPHARM-PDM模型相比，ShapeWorks和Deformetrica形状模型捕获了临床相关的人群水平变异性。
