Retinopathy of prematurity (ROP) is a retinal disorder that may bring about blindness in preterm infants. Early detection and treatment of ROP can prevent this blindness. The gold standard technique for ROP screening is indirect ophthalmoscopy performed by ophthalmologists. The scarcity of medical professionals and inter-observer heterogeneity in ROP grading are two of the screening concerns. Researchers employ artificial intelligence (AI) driven ROP screening systems to assist medical experts. A major hurdle in developing these systems is the unavailability of annotated data sets of fundus images. Anatomical landmarks in the retina, such as the optic disc, macula, blood vessels, and ridge, are used to identify ROP characteristics. HVDROPDB is the first dataset to be published for the retinal structure segmentation of fundus images of preterm infants. It is prepared from two diverse imaging systems on the Indian population for segmenting the lesions mentioned above and annotated by a group of ROP experts. Each dataset contains retinal fundus images of premature infants with the ground truths prepared manually to assist researchers in developing explainable automated screening systems.
Keywords: Dataset; HVDROPDB; Retinopathy of prematurity; Segmentation.
© 2023 The Authors. Published by Elsevier Inc.