Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Anupama Goparaju, Alexandre Bone, Nan Hu, Heath B. Henninger, Andrew, E. Anderson, Stanley Durrleman, Matthijs Jacxsens, Alan Morris, Ibolya Csecs,, Nassir Marrouche, Shireen Y. Elhabian

TL;DR
This paper systematically evaluates and compares popular statistical shape modeling tools in clinical contexts, highlighting their relative consistency and ability to capture relevant anatomical variability.
Contribution
It introduces validation frameworks for anatomical landmark inference and lesion screening, and assesses the performance of ShapeWorks, Deformetrica, and SPHARM-PDM in clinical applications.
Findings
ShapeWorks and Deformetrica are more consistent than SPHARM-PDM.
ShapeWorks and Deformetrica better capture clinically relevant variability.
Different tools show varying levels of shape model consistency.
Abstract
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…
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