Terzopoulos D, McInerney T
Department of Computer Science, University of Toronto, ON, Canada. dt
Stud Health Technol Inform. 1997;39:369-78.
Deformable models are a popular and vigorously researched model-based approach to computer-assisted medical image analysis. The widely recognized efficacy of deformable models stem from their ability to segment, match and track images of anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size and shape of structures of interest. Deformable models are capable of accommodating the often significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task as necessary. In this paper we will review deformable models and present some recent developments in the methodology, including topologically adaptable deformable models, an approach that permits segmentation and reconstruction of topologically complex anatomical structures.
可变形模型是一种广受欢迎且得到大量研究的基于模型的计算机辅助医学图像分析方法。可变形模型得到广泛认可的有效性源于其通过利用（自下而上）从图像数据中得出的约束以及（自上而下）关于感兴趣结构的位置、大小和形状的先验知识，对解剖结构图像进行分割、匹配和跟踪的能力。可变形模型能够适应生物结构随时间和不同个体而经常出现的显著变化。此外，它们支持高度直观的交互机制，使医学科学家和从业者能够在必要时将其专业知识应用于基于模型的图像解释任务。在本文中，我们将回顾可变形模型，并介绍该方法的一些最新进展，包括拓扑适应性可变形模型，这是一种允许对拓扑复杂的解剖结构进行分割和重建的方法。
