Abstract：
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.
摘要：
视网膜图像配准由于其在医学实践中的广泛应用而至关重要。在这种情况下，我们提议ConKeD,一种新的深度学习方法来学习用于视网膜图像配准的描述符。与当前的注册方法相比，我们的方法采用了一种新颖的多正多负对比学习策略,该策略可以利用可用训练样本中的其他信息.这使得可以从有限的训练数据中学习高质量的描述符。为了训练和评估ConKeD，我们将这些描述符与特定领域的关键点结合起来，特别是血管分叉和交叉，使用深度神经网络检测。我们的实验结果证明了新的多积极多消极策略的好处，因为它优于广泛使用的三重态损失技术（单正和单负）以及单正多负替代方案。此外,ConKeD与特定领域关键点的组合产生与最先进的视网膜图像配准方法相当的结果，同时提供重要的优势，如避免预处理，利用更少的训练样本，并且需要更少的检测到的关键点，在其他人中。因此,ConKeD显示出促进基于深度学习的视网膜图像配准方法的开发和应用的潜力。
