COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

TL;DR

COph100 is a new, challenging dataset of infant retinal images with diverse quality issues, designed to improve registration methods and support pediatric ophthalmology research.

Contribution

The paper introduces COph100, a comprehensive infant retinal image dataset with ground truth labels, addressing limitations of existing adult-focused datasets.

Findings

Assessed dataset quality and registration performance with state-of-the-art algorithms.

Provides a valuable resource for benchmarking retinal registration methods.

Facilitates analysis of disease progression in infants.

Abstract

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions.