综述医学影像纵向形状建模方法，涵盖微分同胚与深度学习技术。
Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learning
- 从微分同胚映射到生成网络，系统梳理形状演变建模方法
- 指出当前方法在非线性动态建模中的局限性与不足
- 适合医学图像分析、生物形态演化研究者参考
活体生物组织是复杂系统，会随内外刺激持续生长和变化，导致解剖结构形状发生显著而复杂的演变。对自然及病理状态下的形状变化进行建模与理解，在诊断、预后和治疗中具有重要意义。然而，由于其内在的非线性特征，建模生物组织的纵向形状变化仍具挑战。本文综述了现有用于时空形状建模的方法与工具，涵盖从微分同胚度量映射到基于深度学习的模型（如自编码器、生成网络、循环神经网络等）。讨论了现有技术的协同融合潜力及未来研究方向，指出了当前研究中的关键缺陷。
原文摘要 · Abstract (English)
Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。