Retinal IPA：基于迭代关键点对齐的多模态视网膜成像
摘要
我们提出了一个 novel framework for retinal feature point alignment, designed for learning cross-modality features to enhance matching and registration across multi-modality retinal images. Our model draws on the success of previous learning-based feature detection and description methods. To better leverage unlabeled data and constrain the model to reproduce relevant keypoints, we integrate a keypoint-based segmentation task. It is trained in a self-supervised manner by enforcing segmentation consistency between different augmentations of the same image. By incorporating a keypoint augmented self-supervised layer, we achieve robust feature extraction across modalities. Extensive evaluation on two public datasets and one in-house dataset demonstrates significant improvements in performance for modality-agnostic retinal feature alignment. Our code and model weights are publicly available at \url{https://github.com/MedICL-VU/RetinaIPA}.
引用
@article{arxiv.2407.18362,
title = {Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging},
author = {Jiacheng Wang and Hao Li and Dewei Hu and Rui Xu and Xing Yao and Yuankai K. Tao and Ipek Oguz},
journal= {arXiv preprint arXiv:2407.18362},
year = {2024}
}