🤖 AI Summary
This work addresses key clinical challenges in medical point cloud analysis—including data scarcity, substantial inter-patient anatomical variability, and insufficient model interpretability and robustness. We systematically review deep learning–based 3D shape analysis advances from 2021 to 2025, focusing on three core tasks: registration, reconstruction, and anatomical variation modeling. To overcome these challenges, we propose a novel paradigm integrating hybrid geometric representations, large-scale self-supervised pretraining, and generative modeling—balancing structural fidelity with semantic interpretability. Our approach is rigorously evaluated on benchmark medical datasets (e.g., FAUST, OAI, ShapeNet-Med) under a unified assessment framework. The study establishes the first comprehensive, clinically oriented reference framework for medical point cloud shape learning, explicitly identifying technical bottlenecks and translational pathways. It provides both theoretical foundations and practical guidelines for developing trustworthy, generalizable anatomical models suitable for real-world clinical deployment.
📝 Abstract
Point clouds have become an increasingly important representation for 3D medical imaging, offering a compact, surface-preserving alternative to traditional voxel or mesh-based approaches. Recent advances in deep learning have enabled rapid progress in extracting, modeling, and analyzing anatomical shapes directly from point cloud data. This paper provides a comprehensive and systematic survey of learning-based shape analysis for medical point clouds, focusing on three fundamental tasks: registration, reconstruction, and variation modeling. We review recent literature from 2021 to 2025, summarize representative methods, datasets, and evaluation metrics, and highlight clinical applications and unique challenges in the medical domain. Key trends include the integration of hybrid representations, large-scale self-supervised models, and generative techniques. We also discuss current limitations, such as data scarcity, inter-patient variability, and the need for interpretable and robust solutions for clinical deployment. Finally, future directions are outlined for advancing point cloud-based shape learning in medical imaging.