Fan Xin, Li Zi, Li Ziyang, Wang Xiaolin, Liu Risheng, Luo Zhongxuan, Huang Hao
IEEE Trans Image Process. 2023;32:4880-4892. doi: 10.1109/TIP.2023.3307215. Epub 2023 Sep 1.
Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks, requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures with respect to medical data available for a given registration task/scenario. This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts to conveniently find off-the-shelf registration algorithms for various registration scenarios. Specifically, we establish a triple-level framework to embrace the searching for both network architectures and objectives with a cooperating optimization. Extensive experiments on multiple volumetric datasets and various registration scenarios demonstrate that AutoReg can automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance. The automatically learned network also improves computational efficiency over the mainstream UNet architecture from 0.558 to 0.270 seconds for a volume pair on the same configuration.
可变形图像配准在医学图像分析的各种任务中起着关键作用。一个成功的配准算法，无论是源自传统的能量优化还是深度网络，都需要计算机专家付出巨大努力来精心设计配准能量，或者根据给定配准任务/场景下可用的医学数据仔细调整网络架构。本文提出了一种自动学习配准算法（AutoReg），它能协同优化架构及其相应的训练目标，使非计算机专家能够方便地为各种配准场景找到现成的配准算法。具体而言，我们建立了一个三级框架，通过协同优化来同时搜索网络架构和目标。在多个体积数据集和各种配准场景上进行的大量实验表明，AutoReg可以为给定体积自动学习一个最优的深度配准网络，并实现当前最优性能。在相同配置下，对于一对体积数据，自动学习的网络相对于主流的UNet架构还将计算效率从0.558秒提高到了0.270秒。
