A deep learning framework for unsupervised affine and deformable image registration
A deep learning framework for unsupervised affine and deformable image registration
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DOI：
10.1016/j.media.2018.11.010
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发表时间：
2019-02-01
影响因子：
10.9
通讯作者：
Isgum, Ivana
中科院分区：
文献类型：
--
作者：
de Vos, Bob D.;Berendsen, Floris F.;Isgum, Ivana
图像配准是将两幅或多幅图像对齐的过程，是许多（半）自动医学图像分析任务的核心技术。最近的研究表明，深度学习方法，特别是卷积神经网络（ConvNets），可以用于图像配准。到目前为止，用于配准的ConvNets的训练是使用预定义的示例配准来监督的。然而，获得示例注册并不简单。为了避免对预定义示例的需求，从而提高训练ConvNets进行图像配准的便利性，我们提出了用于无监督仿射和可变形图像配准的深度学习图像配准（DLIR）框架。在DLIR框架中，通过利用类似于传统的基于强度的图像配准的图像相似性来训练ConvNets进行图像配准。在使用DLIR框架训练ConvNet之后，它可以用于在一个镜头中注册成对的不可见图像。我们提出了灵活的ConvNets设计，用于仿射图像配准和可变形图像配准。通过将多个ConvNet堆叠到一个更大的架构中，我们能够执行从粗到精的图像配准。我们表明，对于心脏电影MRI和胸部CT的配准，DLIR框架的性能与传统的图像配准相当，同时快了几个数量级。(C)2018年爱思唯尔通过。All rights reserved.
Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster. (C) 2018 Elsevier By. All rights reserved.