系统梳理医学点云形状学习的三大核心任务与前沿进展。
A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation
- 聚焦注册、重建与变异建模三类任务，整合近年深度学习方法。
- 总结2021–2025年代表性模型、数据集与评估指标，涵盖临床应用。
- 揭示数据稀缺与可解释性挑战，适合医疗影像与点云研究者参考。
点云已成为3D医学影像的重要表示方式，相比传统体素或网格方法更具紧凑性与表面保真性。深度学习的进展使从点云数据中直接提取、建模和分析解剖结构形状成为可能。本文系统综述了2021至2025年间基于学习的医学点云形状分析方法，重点涵盖三个基础任务：配准、重建与变异建模。我们梳理了相关文献，总结代表性方法、数据集与评估指标，并突出其在临床中的应用及医学领域的独特挑战。关键趋势包括混合表示融合、大规模自监督模型以及生成式技术的应用。同时指出当前局限，如数据稀缺、患者间差异大，以及临床部署所需的可解释性与鲁棒性。最后，展望了推动医学影像点云学习发展的未来方向。
原文摘要 · Abstract (English)
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.
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