3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing). The ground truth for 3D medical images is very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this paper, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), to register 3D medical images. Three technical components ameliorate our unsupervised learning system for 3D end-to-end medical image registration: (1) We cascade the registration subnetworks; (2) We integrate affine registration into our network; and (3) We incorporate an additional invertibility loss into the training process. Experimental results demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-the-art performance in medical image registration.
翻译:3D医学图象登记具有极大的临床重要性。然而,监督的学习方法需要大量准确的附加说明的相应控制点(或变形),3D医学图象的地面真实性很难获得。无监督的学习方法通过在无监督的情况下利用未贴标签的数据来减轻人工注解的负担。在本文中,我们提议了一种新的未经监督的学习方法,在端到端的框架内使用进化神经网络,即立体图象登记。三个技术组成部分改进了我们用于3D端至端医学图象登记的未经监督的学习系统:(1)我们将注册子网络升级;(2)我们将亲子登记纳入我们的网络;(3)我们将更多的不可视性损失纳入培训过程。实验结果表明,我们的算法比传统的优化方法更快880x(或不加速使用GPU),并且实现了医学图象登记方面的最先进的表现。