COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database
This addresses a gap in pediatric ophthalmology by providing a dataset for infants with image quality issues, though it is incremental as it focuses on data creation rather than new methods.
The authors tackled the lack of public datasets for retinal image registration in infants by introducing COph100, a comprehensive dataset with 100 eyes and 491 image pairs, which includes manual ground truth labels and automatic vessel segmentation masks to enable robust methodology comparison and disease progression analysis.
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.