MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance

2026-04-13

AI Summary

This work addresses the limitations of traditional statistical shape modeling, which relies on dense annotations and fixed latent representations, thereby struggling to flexibly capture complex anatomical variations. The authors propose MorphoFlow, a framework that learns compact probabilistic shape representations from only sparse surface annotations. MorphoFlow integrates neural implicit representations, a self-decoder architecture, and autoregressive normalizing flows, augmented with an adaptive latent correlation weighting mechanism. This mechanism leverages a sparsity-inducing prior to automatically modulate the contribution of each latent dimension to anatomical variability, eliminating the need for manual hyperparameter tuning. The method enables high-resolution 3D shape generation and uncertainty quantification. Evaluated on lumbar spine and femur datasets, MorphoFlow achieves high-fidelity reconstructions and accurately recovers population-consistent, structured patterns of anatomical variation.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed latent representations. These requirements limit scalability and reduce flexibility when modeling complex anatomical variation. We introduce MorphoFlow, a sparse supervised generative shape modeling framework that learns compact probabilistic shape representations directly from sparse surface annotations. MorphoFlow integrates neural implicit shape representations with an autodecoder formulation and autoregressive normalizing flows to learn an expressive probabilistic density over the latent shape space. The neural implicit representation enables resolution-agnostic modeling of 3D anatomy, while the autodecoder formulation supports direct optimization of per-instance latent codes under sparse supervision. The autoregressive flow captures the distribution of latent anatomical variability providing a tractable, likelihood-based generative model of shapes. To promote compact and structured latent representations, we incorporate adaptive latent relevance weighting through sparsity-inducing priors, enabling the model to regulate the contribution of individual latent dimensions according to their relevance to the underlying anatomical variation while preserving generative expressivity. The resulting latent space supports uncertainty quantification and anatomically plausible shape synthesis without manual latent dimensionality tuning. Evaluation on publicly available lumbar vertebrae and femur datasets demonstrates accurate high-resolution reconstruction from sparse inputs and recovery of structured modes of anatomical variation consistent with population level trends.

Problem

• statistical shape modeling
• sparse supervision
• anatomical variability
• latent representation
• 3D shape reconstruction

Innovation

• sparse-supervised learning
• neural implicit representation
• autodecoder
• normalizing flows
• adaptive latent relevance