Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.

翻译：视网膜图像配准因其在医疗实践中的广泛应用而至关重要。针对此问题，本文提出ConKeD，一种用于学习视网膜图像配准描述子的新型深度学习方法。与当前配准方法不同，我们的方法采用了一种新颖的多正例多负例对比学习策略，能够从可用训练样本中挖掘额外信息，从而在有限训练数据下学习高质量描述子。为训练和评估ConKeD，我们将这些描述子与通过深度神经网络检测的领域特定关键点（特别是血管分叉点和交叉点）相结合。实验结果表明，新颖的多正例多负例策略优于广泛使用的三元组损失技术（单正例单负例）以及单正例多负例替代方案。此外，ConKeD与领域特定关键点的组合能够产生与视网膜图像配准前沿方法可比的结果，同时具备避免预处理、使用更少训练样本和减少所需关键点数量等重要优势。因此，ConKeD在推动基于深度学习的视网膜图像配准方法开发与应用方面展现出巨大潜力。