AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks
This work addresses the need for fast and accurate image registration in medical imaging, particularly for brain scans, though it appears incremental as it builds on existing neural network approaches.
The paper tackles the problem of affine registration for 3D medical images by proposing AIRNet, a self-supervised method that directly estimates transformation parameters without using a metric as an optimizing function, achieving better overall performance and a 100x speed-up compared to conventional methods.
In this work, we propose a self-supervised learning method for affine image registration on 3D medical images. Unlike optimisation-based methods, our affine image registration network (AIRNet) is designed to directly estimate the transformation parameters between two input images without using any metric, which represents the quality of the registration, as the optimising function. But since it is costly to manually identify the transformation parameters between any two images, we leverage the abundance of cheap unlabelled data to generate a synthetic dataset for the training of the model. Additionally, the structure of AIRNet enables us to learn the discriminative features of the images which are useful for registration purpose. Our proposed method was evaluated on magnetic resonance images of the axial view of human brain and compared with the performance of a conventional image registration method. Experiments demonstrate that our approach achieves better overall performance on registration of images from different patients and modalities with 100x speed-up in execution time.