End-to-end affine registration framework for histopathological images with weak annotations

Background and Objective: Histopathological image registration is an essential component in digital pathology and biomedical image analysis. Deep-learning-based algorithms have been proposed to achieve fast and accurate affine registration. Some previous studies assume that the pairs are free from sizeable initial position misalignment and large rotation angles before performing the affine transformation. However, large-rotation angles are often introduced into image pairs during the production process in real-world pathology images. Reliable initial alignment is important for registration performance. The existing deep-learning-based approaches often use a two-step affine registration pipeline because convolutional neural networks (CNNs) cannot correct large-angle rotations. Methods: In this manuscript, a general framework ARoNet is developed to achieve end-to-end affine registration for histopathological images. We use CNNs to extract global features of images and fuse them to construct correspondent information for affine transformation. In ARoNet, a rotation recognition network is implemented to eliminate great rotation misalignment. In addition, a self-supervised learning task is proposed to assist the learning of image representations in an unsupervised manner. Results: We applied our model to four datasets, and the results indicate that ARoNet surpasses existing affine registration algorithms in alignment accuracy when large angular misalignments (e.g., 180 rotation) are present, providing accurate affine initialization for subsequent non-rigid alignments. Besides, ARoNet shows advantages in execution time (0.05 per pair), registration accuracy, and robustness. Conclusion: We believe that the proposed general framework promises to simplify and speed up the registration process and has the potential for clinical applications.