In Journal of digital imaging
Accurate registration of lung X-rays is an important task in medical image analysis. However, the conventional methods usually cost a lot in running time, and the existing deep learning methods are hard to deal with the large deformation caused by respiratory and cardiac motion. In this paper, we attempt to use deep learning methods to deal with large deformation and enable it to achieve the accuracy of conventional methods. We proposed the cascading affine and B-spline network (CABN), which consists of convolutional cross-stitch affine block (CCAB) and B-splines U-net-like block (BUB) for large lung motion. CCAB makes use of the convolutional cross-stitch model to learn global features among images. And BUB adopts the idea of cubic B-splines which is suitable for large deformation. We separately demonstrated CCAB, BUB, and CABN on two chest X-ray datasets. The experimental results indicate that our methods are highly competitive both in accuracy and runtime when compared to both other deep learning methods and iterative conventional approaches. Moreover, CCAB also can be used for the preprocessing of non-rigid registration methods, replacing affine in conventional methods.
Chang Qing, Lu Chenhao, Li Mengke
2023-Feb-14
Affine registration, Deep learning, Deformable registration, Medical image registration
