# Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

arXiv: https://arxiv.org/abs/2009.02878 · HTML: https://mlsanity.com/paper/2009.02878/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models](https://mlsanity.com/paper/2110.07152/) (2021)
- [Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud](https://mlsanity.com/paper/2305.14486/) (2023)
- [S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences](https://mlsanity.com/paper/2304.07515/) (2023)
- [Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds](https://mlsanity.com/paper/2405.09707/) (2024)
- [Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction](https://mlsanity.com/paper/2309.04956/) (2023)
- [Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images](https://mlsanity.com/paper/2405.09697/) (2024)
- [Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models](https://mlsanity.com/paper/2310.01529/) (2023)
- [Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries](https://mlsanity.com/paper/2209.02706/) (2022)
- [On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling](https://mlsanity.com/paper/2407.15260/) (2024)
- [Adaptive Particle-Based Shape Modeling for Anatomical Surface Correspondence](https://mlsanity.com/paper/2507.07379/) (2025)
- [A Statistical 3D Stomach Shape Model for Anatomical Analysis](https://mlsanity.com/paper/2509.06464/) (2025)

## Abstract (arXiv)

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.