Diffeomorphic image registration, offering smooth transformation and topology preservation, is required in many medical image analysis tasks.Traditional methods impose certain modeling constraints on the space of admissible transformations and use optimization to find the optimal transformation between two images. Specifying the right space of admissible transformations is challenging: the registration quality can be poor if the space is too restrictive, while the optimization can be hard to solve if the space is too general. Recent learning-based methods, utilizing deep neural networks to learn the transformation directly, achieve fast inference, but face challenges in accuracy due to the difficulties in capturing the small local deformations and generalization ability. Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations. A multilayer perceptron (MLP) with sinusoidal activation function is used to represent the continuous velocity field and assigns a velocity vector to every point in space, providing the flexibility of modeling complex deformations as well as the convenience of optimization. Moreover, we propose a cascaded image registration framework (Cas-DNVF) by combining the benefits of both optimization and learning based methods, where a fully convolutional neural network (FCN) is trained to predict the initial deformation, followed by DNVF for further refinement. Experiments on two large-scale 3D MR brain scan datasets demonstrate that our proposed methods significantly outperform the state-of-the-art registration methods.
翻译:在许多医学图像分析任务中,需要进行平滑的变异和地形保存的变异图像登记。 传统方法对可允许的变异空间施加某些模型限制, 并使用优化来寻找两种图像之间的最佳变异。 指定可允许变异的正确空间具有挑战性: 如果空间太狭窄, 登记质量可能较差, 而如果空间太笼统, 优化则难以解决。 最近基于学习的方法, 利用深神经网络直接学习变异, 实现快速推导, 但由于难以捕捉小地方变异和一般化能力, 而在准确性方面面临着挑战。 我们在这里提出了一个新的基于优化的优化方法, 名为 DNVF( 以神经变异形图像登记和神经变异异变速度场的登记) 。 此外, 我们提议了一个基于直观活变异形激活功能的多层摄像系统( 多级感应代表连续速度场场, 并给空间的每个点指派一个速度矢量矢量矢量矢量矢量矢量矢量矢量矢量矢量, 提供模拟的复杂变异变异变变变的灵活度, 以及内部变异变变变变变现的初始精度优化便利。