School of Information Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China.
Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, Henan, China.
J Healthc Eng. 2021 Jul 26;2021:5528160. doi: 10.1155/2021/5528160. eCollection 2021.
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.
医学图像配准的目的是找到使两幅医学图像对齐的几何变换，以使两幅图像上的对应体素在空间上一致。非刚性医学图像配准是医学图像处理的关键步骤，例如图像比较、数据融合、目标识别和病理变化分析。现有的配准方法仅考虑配准精度，但在很大程度上忽略了配准结果的不确定性。在这项工作中，提出了一种基于贝叶斯全卷积神经网络的非刚性医学图像配准方法。所提出的方法可以生成一个几何不确定性图来计算配准结果的不确定性。这种不确定性可以解释为置信区间，这对于判断源数据是否异常至关重要。此外，所提出的方法引入了组归一化，这有利于贝叶斯神经网络的网络收敛。在不同的图像数据集上，将一些有代表性的基于学习的图像配准方法与所提出的方法进行了比较。实验结果表明，所提出的方法的配准精度优于其他方法，其抗折叠性能与快速图像配准和 VoxelMorph 相当。此外，所提出的方法可以评估配准结果的不确定性。
