Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies and computational anatomy tasks such as registration and segmentation. Because developing a template is a computationally expensive process, relatively few templates are available. As a result, analysis is often conducted with sub-optimal templates that are not truly representative of the study population, especially when there are large variations within this population. We propose a machine learning framework that uses convolutional registration neural networks to efficiently learn a function that outputs templates conditioned on subject-specific attributes, such as age and sex. We also leverage segmentations, when available, to produce anatomical segmentation maps for the resulting templates. The learned network can also be used to register subject images to the templates. We demonstrate our method on a compilation of 3D brain MRI datasets, and show that it can learn high-quality templates that are representative of populations. We find that annotated conditional templates enable better registration than their unlabeled unconditional counterparts, and outperform other templates construction methods.

翻译：可变形模板，或称图谱，是代表特定群体典型解剖结构的图像，通常辅以概率解剖标签图。它们在医学图像分析中广泛应用于群体研究和计算解剖学任务，如配准与分割。由于构建模板的计算成本高昂，现有可用模板相对较少。因此，分析常使用非最优模板，这些模板无法真实代表研究群体，尤其在群体内部存在较大变异时。我们提出一种机器学习框架，利用卷积配准神经网络高效学习一个函数，该函数可根据个体特定属性（如年龄和性别）输出条件化模板。当分割标签可用时，我们还利用其生成所得模板的解剖分割图。训练后的网络亦可用于将个体图像配准至模板。我们在整合的三维脑部MRI数据集上验证了该方法，证明其能够学习到具有群体代表性的高质量模板。研究发现，带标注的条件模板相较于无标注的无条件模板能实现更优的配准效果，且性能优于其他模板构建方法。