Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging

• URL: http://arxiv.org/abs/2407.18362v1
• Date: Thu, 25 Jul 2024 19:51:27 GMT
• Title: Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
• Authors: Jiacheng Wang, Hao Li, Dewei Hu, Rui Xu, Xing Yao, Yuankai K. Tao, Ipek Oguz,
• Abstract summary: We propose a novel framework 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. It is trained in a self-supervised manner by enforcing segmentation consistency between different augmentations of the same image.
• Score: 11.70130626541926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a 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}.

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