The 3D deformable image registration is one of the most challenging tasks in medical image analysis. Due to the large and complex deformation in 3D medical images, many deep neural network based methods have been proposed to improve the image similarity after registration, among which recursive cascading network structure is one of the state-of-the-art. However, most existing works rely on the pixel-level image similarities to achieve anatomical rationality and overlook the global-level resemblance between the two structures. Therefore, the resulting registration is not quite clinically valuable. To this end, in this work, we propose a Progressive Anatomically Constrained deep neural Network (PACN) to incorporate the anatomical priors into a progressive cascading registration network to improve the anatomical plausibility as well as the pixel-level similarity of the registration results. Specifically, an Anatomical Constraint Encoder (ACE) network is proposed to encode the global context of the anatomical segmentations and attached to the dense registration network to form a registration unit. Repeated such units forming a cascading framework progressively warps the moving image toward the fixed one, with the output warped image of one unit as the input of the next unit. In this design, the global anatomical priors along with the pixel-level local information are used to guide the model learning process to produce high quality deformation field. Based on this, we explore two frameworks to investigate their registration effectiveness, one attaches the anatomical constraint encoder (ACE) to every dense registration sub-network and the other one attaches ACE only to the last dense registration unit. We test the two frameworks on benchmarks of three liver image datasets SLIVER, LiTS and LSPIG, and one brain dataset LPBA. Our two frameworks have achieved significantly better results in terms of average Dice score than the state-of-the-art baseline method on three liver datasets and comparable on LPBA when both tested with up to three cascades.

3D 可变形图像配准是医学图像分析中最具挑战性的任务之一。由于3D医学图像中的大而复杂的变形，已经提出了许多基于深度神经网络的方法来提高配准后的图像相似度，其中递归级联网络结构是最先进的方法之一。然而，大多数现有工作依靠像素级图像相似性来实现解剖合理性，而忽略了两种结构之间全局级的相似性。因此，由此产生的配准在临床上并不十分有价值。为此，在这项工作中，我们提出了一种渐进式解剖学约束深度神经网络（PACN），将解剖学先验合并到渐进式级联配准网络中，以提高配准结果的解剖合理性和像素级相似性。具体来说，提出了一种解剖约束编码器（ACE）网络来对解剖分割的全局上下文进行编码，并附加到密集配准网络上以形成配准单元。形成级联框架的重复此类单元将运动图像逐渐向固定图像扭曲，一个单元的输出扭曲图像作为下一个单元的输入。在这个设计中，全局解剖先验和像素级局部信息被用来指导模型学习过程以产生高质量的变形场。基于此，我们探索了两种框架来研究它们的注册有效性，一种将解剖约束编码器 (ACE) 附加到每个密集注册子网络，另一种将 ACE 仅附加到最后一个密集注册单元。我们在三个肝脏图像数据集 SLIVER、LiTS 和 LSPIG 以及一个大脑数据集 LPBA 的基准测试上测试了这两个框架。在三个肝脏数据集上，我们的两个框架在平均 Dice 得分方面取得了比最先进的基线方法更好的结果，并且当两者都使用多达三个级联进行测试时，在 LPBA 上具有可比性。我们在三个肝脏图像数据集 SLIVER、LiTS 和 LSPIG 以及一个大脑数据集 LPBA 的基准测试上测试了这两个框架。在三个肝脏数据集上，我们的两个框架在平均 Dice 得分方面取得了比最先进的基线方法更好的结果，并且当两者都使用多达三个级联进行测试时，在 LPBA 上具有可比性。我们在三个肝脏图像数据集 SLIVER、LiTS 和 LSPIG 以及一个大脑数据集 LPBA 的基准测试上测试了这两个框架。在三个肝脏数据集上，我们的两个框架在平均 Dice 得分方面取得了比最先进的基线方法更好的结果，并且当两者都使用多达三个级联进行测试时，在 LPBA 上具有可比性。