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Deformable medical image registration based on CNN
1Department of Mathematics and Statistics, Northeast Petroleum University, Daqing, China.
Objective:
This study aims to develop and test a new image registration method in which full-scale skip connections in the encoding process is added into the registration network, which can predict the deformation field more accurately by retaining more features and information in the decoding process.
Methods:
Two improved registration networks are connected in series in the registration framework. Each registration network uses the unsupervised learning registration method to predict a small deformation field, and the last two small deformation fields are superimposed to obtain the final deformation field. The model is evaluated by the oasis datasets (brain T1-weighted MRI images), one image is selected as the fixed image, while 383 images are used as training images and 30 images are used as test images. Wavelet decomposition and reconstruction are also used to enhance the image.
Results:
Compared with the affine method, the voxelmorph-1 method and the voxelmorph-2 method, applying the new registration network was proposed by this study improves the registration accuracy by 28.6%, 1.2% and 0.2%, respectively.
Conclusion:
The experimental results demonstrate that the method proposed in this study can improve the accuracy of image registration effectively.
