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Joint keypoint detection and description network for color fundus image registration
David Rivas-Villar1,2, Álvaro S Hervella1,2, José Rouco1,2
1VARPA Group, A Coruña Biomedical Research Institute (INIBIC), University of A Coruña, Xubias de Arriba, A Coruña, Spain.
Quantitative Imaging in Medicine and Surgery
|July 17, 2023
Summary
This study introduces a new deep learning method for aligning retinal images, improving disease monitoring. The approach enhances accuracy, especially in cases of disease progression, outperforming traditional techniques.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Retinal imaging is crucial for diagnosing eye and systemic diseases.
- Image registration aligns images for change assessment, vital for disease monitoring.
- Classical methods dominate color fundus registration, but deep learning offers adaptability.
Purpose of the Study:
- To develop a novel deep learning methodology for accurate color fundus image registration.
- To enable joint detection and description of keypoints for improved alignment.
- To create a registration method robust to variations in imaging devices and conditions.
Main Methods:
- Utilized an unsupervised neural network for repeatable keypoint detection and reliable descriptor generation.
- Employed RANdom SAmple Consensus (RANSAC) for accurate registration based on detected keypoints and descriptors.
- Trained the model on the Messidor dataset and validated on the Fundus Image Registration Dataset (FIRE).
Main Results:
- Achieved an overall Registration Score of 0.695 on the FIRE dataset.
- Demonstrated high performance in specific categories: 0.925 (S), 0.352 (P), and 0.726 (A).
- The method proved robust to variations in imaging devices and capture conditions.
Conclusions:
- The proposed deep learning method enhances color fundus image registration.
- It outperforms previous deep learning methods and classical approaches, particularly in disease progression scenarios (Category A).
- This advancement is significant for clinical practice, aiding disease monitoring and management.
