Registration-based atlas building often poses computational challenges in high-dimensional image spaces. In this paper, we introduce a novel hybrid atlas building algorithm that fast estimates atlas from large-scale image datasets with much reduced computational cost. In contrast to previous approaches that iteratively perform registration tasks between an estimated atlas and individual images, we propose to use learned priors of registration from pre-trained neural networks. This newly developed hybrid framework features several advantages of (i) providing an efficient way of atlas building without losing the quality of results, and (ii) offering flexibility in utilizing a wide variety of deep learning based registration methods. We demonstrate the effectiveness of this proposed model on 3D brain magnetic resonance imaging (MRI) scans.

翻译：以注册为基础的地图集建设往往给高维图像空间带来计算挑战。 在本文中,我们引入了新型混合地图集建设算法,快速估算大型图像数据集的地图集,而计算成本则大大降低。与以往在估计的地图集和个人图像之间迭接履行登记任务的做法不同,我们提议使用预先培训的神经网络所学的注册前科。这一新开发的混合框架具有以下几个优点:(一) 提供有效的地图集构建方法,同时不丧失结果质量,以及(二) 提供灵活性,使用各种深层学习的登记方法。我们展示了3D脑磁共振成像(MRI)扫描的这一拟议模型的有效性。