  Statistical shape modeling (SSM) is widely used in biology and medicine as a
new generation of morphometric approaches for the quantitative analysis of
anatomical shapes. Technological advancements of in vivo imaging have led to
the development of open-source computational tools that automate the modeling
of anatomical shapes and their population-level variability. However, little
work has been done on the evaluation and validation of such tools in clinical
applications that rely on morphometric quantifications (e.g., implant design
and lesion screening). Here, we systematically assess the outcome of widely
used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and
SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape
models from different tools. We propose validation frameworks for anatomical
landmark/measurement inference and lesion screening. We also present a lesion
screening method to objectively characterize subtle abnormal shape changes with
respect to learned population-level statistics of controls. Results demonstrate
that SSM tools display different levels of consistencies, where ShapeWorks and
Deformetrica models are more consistent compared to models from SPHARM-PDM due
to the groupwise approach of estimating surface correspondences. Furthermore,
ShapeWorks and Deformetrica shape models are found to capture clinically
relevant population-level variability compared to SPHARM-PDM models.
