Retinal image registration (RIR) plays an important role in the diagnosis and long-term monitoring of retinal diseases. Retinal image global registration (RIGR) is usually the first step of RIR. Traditional methods often struggle to achieve robust keypoint detection and description when faced with high-resolution, fine-textured retinal images. Deep learning-based methods for this task have not been widely developed. Therefore, we propose a keypoint detection and description network based on local feature saliency, EyeKey, for RIGR. EyeKey uses the "Detect While Describing (DWD)" design. Specifically, two proposed UDPAM++ modules are embedded into the feature description network to enhance its feature description capability. Concurrently, these modules detect distinctive keypoints based on local feature saliency, combined with a Mapping Module featuring only three learnable parameters. Moreover, we achieve self-supervised feature description network training on high-resolution, fine-textured retinal images through the Random Local Hardest Example Mining strategy. Additionally, we realize robust unsupervised keypoint detection network training based on the High Matching Probability Defines Keypoints strategy and the proposed Cumulative Salient Keypoint Expansion, which, together with the DWD design, mutually reinforce the training of the keypoint detection and description network. Finally, combined with the feature-based RIGR pipeline, our method achieves outstanding performance while maintaining excellent inference speed on monomodal and multimodal RIGR evaluation datasets.
Liang et al. (Thu,) studied this question.
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