Merck Derek, Tracton Gregg, Saboo Rohit, Levy Joshua, Chaney Edward, Pizer Stephen, Joshi Sarang
Medical Image Display & Analysis Group, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Med Phys. 2008 Aug;35(8):3584-96. doi: 10.1118/1.2940188.
Learning probability distributions of the shape of anatomic structures requires fitting shape representations to human expert segmentations from training sets of medical images. The quality of statistical segmentation and registration methods is directly related to the quality of this initial shape fitting, yet the subject is largely overlooked or described in an ad hoc way. This article presents a set of general principles to guide such training. Our novel method is to jointly estimate both the best geometric model for any given image and the shape distribution for the entire population of training images by iteratively relaxing purely geometric constraints in favor of the converging shape probabilities as the fitted objects converge to their target segmentations. The geometric constraints are carefully crafted both to obtain legal, nonself-interpenetrating shapes and to impose the model-to-model correspondences required for useful statistical analysis. The paper closes with example applications of the method to synthetic and real patient CT image sets, including same patient male pelvis and head and neck images, and cross patient kidney and brain images. Finally, we outline how this shape training serves as the basis for our approach to IGRT/ART.
学习解剖结构形状的概率分布需要将形状表示与医学图像训练集中人类专家的分割结果进行拟合。统计分割和配准方法的质量直接取决于这种初始形状拟合的质量，然而该主题在很大程度上被忽视或只是以一种临时的方式进行描述。本文提出了一套指导此类训练的通用原则。我们的新方法是通过迭代放宽纯几何约束，以支持随着拟合对象收敛到其目标分割而收敛的形状概率，从而联合估计任何给定图像的最佳几何模型以及整个训练图像群体的形状分布。精心设计几何约束，既能获得合法的、不自我穿透的形状，又能施加有用的统计分析所需的模型间对应关系。本文最后给出了该方法在合成和真实患者CT图像集上的示例应用，包括同一患者的男性骨盆、头颈部图像以及跨患者的肾脏和脑部图像。最后，我们概述了这种形状训练如何作为我们的图像引导放疗/自适应放疗方法的基础。
