Brain CT Registration Using Hybrid Supervised Convolutional Neural Network

Abstract Background: Brain computed tomography (CT) image registration is an essential step in the image evaluation of acute cerebrovascular disease (ACVD). Due to the complexity of human brain morphology, low brain CT soft-tissue resolution, low gray/white matter contrast, and the large anatomy variation across individuals, it is still a great challenge to perform brain CT registration accurately and rapidly. This study developed a hybrid supervised convolutional neural network (HSCN-Net) which may be used for assessment of ACVD in brain CT.Method: HSCN-Net generates synthetic deformation fields by a simulator to solve the lack of registration gold standard. The simulator are used to generate multi-scale deformation fields to overcome the registration challenge of large deformation. HSCN-Net adopts a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization ability. In this work, one hundred and one brain CT images were included for HSCN-Net training and evaluation, and the results were compared with Demons and VoxelMorph. Qualitative analysis by visual evaluation, as well as quantitative analysis by Endpoint Error (EPE) between deformation fields, image Normalized Mutual Information (NMI), and Dice coefficient were carried out to access the model performance.Results: Qualitative analysis of HSCN-Net was similar to that of Demons, and both were superior to that of VoxelMorph. Moreover, HSCN-Net was more competent for large and smooth deformations. For quantitative evaluation, the EPE mean of HSCN-Net (3.29 mm) was lower than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the Dice mean of HSCN-Net was 0.96, which was better than that of Demons (0.90) and VoxelMorph (0.87); and the NMI mean of HSCN-Net (0.83) was slightly lower than that of Demons(0.84) but higher than that of VoxelMorph (0.81). In addition, the mean registration time of HSCN-Net (17.86 s) was lower than that of VoxelMorph (18.53 s) and Demons (147.21 s).Conclusion: The proposed hybrid supervised convolution registration network can achieve accurate and rapid brain CT registration. It is helpful for improving image evaluation of ACVD, thereby assisting clinicians in diagnosis and treatment decision-making.