Retinopathy of Prematurity (ROP) is a proliferative retinal vascular disease affecting premature infants with lower birth weights, often leading to vision loss due to retinal detachment. Early diagnosis is crucial to prevent vessel proliferation and associated complications. However, manual diagnosis by ophthalmology professionals is challenging, prone to variability, and lacks standardization. On the other hand, machine learning models trained to perform ROP diagnosis suffer from interpretability issues. To address these challenges, this thesis gives the first steps towards a computational biomarker-based ROP diagnostic system. Recognizing the importance of segmenting the retinal anatomy, and in particular discriminating retinal blood vessels into arteries and veins, we first introduce Momaku, an annotation tool tailored for fundus images.