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Statistical shape modeling (SSM) has proven useful in many areas of biologyand medicine as a new generation of morphometric approaches for thequantitative analysis of anatomical shapes. Recently, the increasedavailability of high-resolution in vivo images of anatomy has led to thedevelopment and distribution of open-source computational tools to modelanatomical shapes and their variability within populations with unprecedenteddetail and statistical power. Nonetheless, there is little work on theevaluation and validation of such tools as related to clinical applicationsthat rely on morphometric quantifications for treatment planning. To addressthis lack of validation, we systematically assess the outcome of widely usedoff-the-shelf SSM tools, namely ShapeWorks, SPHARM-PDM, and Deformetrica, inthe context of designing closure devices for left atrium appendage (LAA) inatrial fibrillation (AF) patients to prevent stroke, where an incomplete LAAclosure may be worse than no closure. This study is motivated by the potentialrole of SSM in the geometric design of closure devices, which could be informedby population-level statistics, and patient-specific device selection, which isdriven by anatomical measurements that could be automated by relatingpatient-level anatomy to population-level morphometrics. Hence, understandingthe consequences of different SSM tools for the final analysis is critical forthe careful choice of the tool to be deployed in real clinical scenarios.Results demonstrate that estimated measurements from ShapeWorks model are moreconsistent compared to models from Deformetrica and SPHARM-PDM. Furthermore,ShapeWorks and Deformetrica shape models capture clinically relevantpopulation-level variability compared to SPHARM-PDM models.

On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application