[论文解读] Non-rigid image registration using fully convolutional networks with deep self-supervision

Hongming Li, Yong Fan|arXiv (Cornell University)|Sep 4, 2017

Medical Image Segmentation Techniques参考文献 17被引用 74

一句话总结

本文提出一种基于全卷积网络（FCN）的非刚性图像配准方法，在多分辨率框架内通过深度自监督，直接估计图像对之间的空间变换。

ABSTRACT

We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. At the same time, our method also learns FCNs for encoding the spatial transformations at the same spatial resolution of images to be registered, rather than learning coarse-grained spatial transformation information. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different resolutions with deep self-supervision through typical feedforward and backpropagation computation. Since our method simultaneously optimizes and learns spatial transformations for the image registration, our method can be directly used to register a pair of images, and the registration of a set of images is also a training procedure for FCNs so that the trained FCNs can be directly adopted to register new images by feedforward computation of the learned FCNs without any optimization. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.

研究动机与目标

• 以深度学习动机化非刚性图像配准，直接估计图像对之间的空间变换。
• 提出一个基于 FCN 的框架，在全图像分辨率下学习变换，而不是粗略表示。
• 将多分辨率优化与深度自监督结合起来，以联合学习变换和编码网络。
• 实现图像对的即时配准，并促进跨图像集的配准训练。

提出的方法

• 使用全卷积网络在与图像相同的分辨率下编码空间变换。
• 在固定图像与经变形的移动图像之间优化图像相似度以引导学习，类似传统配准。
• 实现多分辨率的配准框架，以逐步细化变换和 FCNs。
• 通过反向传播训练，使用来自图像相似度度量的自监督信号。
• 允许将训练好的 FCN 直接应用于新的图像对，无需重复优化。

实验结果

研究问题

• RQ1 FCNs 能否学习高保真、全分辨率的空间变换，用于图像对的非刚性配准？
• RQ2 采用深度自监督的多分辨率训练是否比单分辨率方法在配准精度上有提升？
• RQ3 是否可以通过前馈计算在不需要进一步优化的情况下使用一个配准模型对新的图像对进行配准？

主要发现

• 在 3D 结构性脑部 MRI 图像上显示出优于最先进的配准算法的性能。
• 表明在多分辨率下同时优化变换与 FCN 能获得准确的配准。
• 验证训练好的 FCNs 能通过前馈计算对新图像对进行配准，无需额外优化。