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Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud

Jadie Adams, Shireen Elhabian

TL;DR

This paper addresses the bottleneck of constructing anatomical statistical shape models by proposing Point2SSM, an unsupervised method that learns surface correspondences directly from raw point clouds. It combines a DGCNN-based encoder with an attention-driven module to produce a set of correspondence points via a weighted aggregation of inputs, trained with a loss combining Chamfer distance $CD$ and a pairwise mapping loss ME. Point2SSM is benchmarked against multiple state-of-the-art point-cloud networks and a traditional optimization-based SSM (PSM), demonstrating superior surface sampling accuracy and competitive population statistics across spleen, pancreas, and left atrium datasets, with strong robustness to noise, partial data, and sparse inputs. The work also highlights practical benefits, including fast inference and potential for incremental learning, which broaden the applicability of SSM in clinical research and downstream analyses. Overall, Point2SSM provides a data-driven, scalable alternative to optimization-based SSM and lays the groundwork for more robust, multi-anatomy, and uncertainty-aware anatomical shape modeling.

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.

Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud

TL;DR

This paper addresses the bottleneck of constructing anatomical statistical shape models by proposing Point2SSM, an unsupervised method that learns surface correspondences directly from raw point clouds. It combines a DGCNN-based encoder with an attention-driven module to produce a set of correspondence points via a weighted aggregation of inputs, trained with a loss combining Chamfer distance and a pairwise mapping loss ME. Point2SSM is benchmarked against multiple state-of-the-art point-cloud networks and a traditional optimization-based SSM (PSM), demonstrating superior surface sampling accuracy and competitive population statistics across spleen, pancreas, and left atrium datasets, with strong robustness to noise, partial data, and sparse inputs. The work also highlights practical benefits, including fast inference and potential for incremental learning, which broaden the applicability of SSM in clinical research and downstream analyses. Overall, Point2SSM provides a data-driven, scalable alternative to optimization-based SSM and lays the groundwork for more robust, multi-anatomy, and uncertainty-aware anatomical shape modeling.

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.
Paper Structure (26 sections, 5 equations, 12 figures, 10 tables)

This paper contains 26 sections, 5 equations, 12 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Optimization-based SSM
  4. Deep Learning on Point Clouds
  5. Learning 3D Point Cloud Dense Correspondence
  6. Methods
  7. Proposed Approach: Point2SSM
  8. Comparison Models
  9. Evaluation Metrics
  10. Experiments and Analysis
  11. Results
  12. Robustness Evaluation
  13. Limitations and Future Directions
  14. Conclusion
  15. Point2SSM Ablation Experiment
  16. ...and 11 more sections

Figures (12)

  • Figure 1: Comparison of particle-based modeling (PSM) cates2017shapeworks, point autoencoders (AE) achlioptas2018learning, canonical point autoencoder (CPAE) cheng2021learning, chen2020unsupervised (ISR), deep point correspondence(DPC) lang2021dpc, and Point2SSM.
  • Figure 2: Point2SSM architecture
  • Figure 3: Accuracy metrics are reported with best values outlined. Boxplots show the distribution across test sets and averages are reported to the right. Compactness plots show cumulative population variation captured by PCA modes, larger area under the curve indicates a more compact model.
  • Figure 4: The test examples with median P2F distance output from each model are shown over ground truth meshes. P2F distance is displayed via a color map.
  • Figure 5: The pancreas SSM from Point2SSM is displayed. Point color denotes correspondence. Recoloring according to the distance to a selected point is provided for further illustration. The first four modes of variation are shown for the PSM and Point2SSM model at $\pm 1$ standard deviation from the mean. The heatmap and vector arrows display the distance to the mean.
  • ...and 7 more figures