Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud

TL;DR

Point2SSM is an unsupervised deep learning method that constructs statistical shape models directly from raw point clouds, improving robustness and accuracy in clinical shape analysis.

Contribution

It introduces a novel attention-based module for robust correspondence learning from raw point clouds, enabling direct SSM construction without surface meshes or templates.

Findings

Outperforms existing networks in surface sampling accuracy

More robust to noisy, sparse, or incomplete data

Better captures population-level morphological statistics

Abstract

We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional methods of SSM construction have limitations, including the requirement of noise-free surface meshes or binary volumes, reliance on assumptions or templates, and prolonged inference times due to simultaneous optimization of the entire cohort. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. While deep learning on 3D point clouds has seen success in unsupervised representation learning and shape correspondence, its application to anatomical SSM construction is largely unexplored. We conduct a benchmark of state-of-the-art point cloud deep networks on the SSM task, revealing their limited robustness to clinical challenges such as noisy, sparse, or incomplete input and limited training data. Point2SSM addresses these issues through an attention-based module, providing effective correspondence mappings from learned point features. Our results demonstrate that the proposed method significantly outperforms existing networks in terms of accurate surface sampling and correspondence, better capturing population-level statistics.