2D/3D Medical Image Registration Using Convolutional Neural Network

2D/3D registration is widely used in clinical diagnosis and surgical navigation planning, which can solve the problem of missing information in different dimensions of medical images and assist doctors to accurately locate patients' lesions during surgery. The conventional 2D/3D registration method mainly relies on the gray level of the image for registration, but the registration process is very time consuming, which is not conducive to the clinical real-time requirements, and the registration process is easy to fall into the local optimum. This study proposed a deep learning approach to solve 2D/3D medical image registration problems. The method used a deep learning-based convolutional neural network to train the DRR and automatically learned image features to predict the parameters corresponding to the X-ray image to achieve registration. In the study, the human pelvis model bone was used as the experimental object. A total of 36,000 DRR images were generated as training sets, and 50 X-ray images of the model bone were collected by C arm for verification. Results showed that the test values for the three precision evaluation indicators of the correlation coefficient, normalized mutual information and Euclidean distance were 0. 82±0. 07, 0. 32±0. 03, 61. 56± 10. 91 and the corresponding test values of the conventional 2D/3D algorithm were 0. 79±0. 07, 0. 29±0. 03, 37. 92±7. 24. These results meant the registration accuracy of deep learning algorithm was better than the conventional 2D/3D algorithm and there was no local optimal value for deep learning algorithm. Meanwhile, the registration time of deep learning was about 0. 03 s, which was much lower than the time of conventional 2D/3D registration, which can satisfy the clinical demand for real-time registration. In the future, 2D/3D deep learning registration research of clinical data will be further carried out.