# Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model

arXiv: https://arxiv.org/abs/2201.03481 · HTML: https://mlsanity.com/paper/2201.03481/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images](https://mlsanity.com/paper/1810.00111/) (2018)
- [DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models](https://mlsanity.com/paper/2110.07152/) (2021)
- [Uncertain-DeepSSM: From Images to Probabilistic Shape Models](https://mlsanity.com/paper/2007.06516/) (2020)
- [On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application](https://mlsanity.com/paper/1810.03987/) (2018)
- [Unsupervised Shape Normality Metric for Severity Quantification](https://mlsanity.com/paper/2007.09307/) (2020)

## Built on by (most cited first, 2 of 2)

- [MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images](https://mlsanity.com/paper/2403.11008/) (2024)
- [On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling](https://mlsanity.com/paper/2407.15260/) (2024)

## Abstract (arXiv)

Statistical shape modeling is an essential tool for the quantitative analysis of anatomical populations. Point distribution models (PDMs) represent the anatomical surface via a dense set of correspondences, an intuitive and easy-to-use shape representation for subsequent applications. These correspondences are exhibited in two coordinate spaces: the local coordinates describing the geometrical features of each individual anatomical surface and the world coordinates representing the population-level statistical shape information after removing global alignment differences across samples in the given cohort. We propose a deep-learning-based framework that simultaneously learns these two coordinate spaces directly from the volumetric images. The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models. Additionally, the local correspondences can be used for anatomy segmentation. We demonstrate the efficacy of this joint model for both shape modeling applications on two datasets and its utility in inferring the anatomical surface.