Learning-based stochastic object models for characterizing anatomical variations.
Learning-based stochastic object models for characterizing anatomical variations.
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DOI：
10.1088/1361-6560/aab000
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发表时间：
2018-03-14
影响因子：
3.5
通讯作者：
Li H
中科院分区：
文献类型：
--
作者：
Dolly SR;Lou Y;Anastasio MA;Li H
众所周知，通过计算机模拟基于客观的、基于任务的图像质量测量的成像系统的优化需要使用随机对象模型（SOM）。然而，开发计算上易于处理的SOM，可以准确地模拟人体解剖结构中的统计变化在一个指定的合奏患者仍然是一个具有挑战性的任务。先前报道的数值解剖模型缺乏在广泛的患者群体中准确地对人体解剖结构中的患者间和器官间变化建模的能力，这主要是因为它们建立在与少数患者和个体解剖器官相对应的图像数据上。这可能会在计算机模拟研究中引入体模特定偏倚，其中研究结果在很大程度上取决于使用的体模。然而，在某些应用中，高质量的体积图像和器官轮廓的数据库是可用的，可以促进这种SOM的发展。在这项工作中，开发了一种新的和易于处理的方法，用于学习SOM和从一组体积训练图像生成数值幻影。所提出的方法学习几何属性分布（GAD）的人体解剖器官从广泛的患者群体，其特征在于两个质心之间的关系相邻器官和患者之间的单个器官的解剖形状相似性。通过随机采样学习的质心和形状GAD与从训练数据学习的相应的主要属性变化的约束，可以创建随机对象的集合。器官形状和位置的随机性反映了人体解剖学的可变性。为了证明的方法，SOM的成年男性骨盆计算和相应的数字幻影的例子创建。
It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.
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