Zengin, HasanCamara, JoséCoelho, PauloRodrigues, João M. F.Cunha, António2025-10-142025-10-142020-07Zengin, H., Camara, J., Coelho, P., Rodrigues, J.M.F., Cunha, A. (2020). Low-Resolution Retinal Image Vessel Segmentation. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Applications and Practice. HCII 2020. Lecture Notes in Computer Science(), vol 12189. Springer, Cham. https://doi.org/10.1007/978-3-030-49108-6_44.978303049107997830304910860302-9743http://hdl.handle.net/10400.8/14256EISBN - 9783030491086Conference name - 14th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020; Conference date - 19 July 2020 - 24 July 2020; Conference code - 242149Segmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547.engFaster R-CNNU-NetLow-resolution retinal imagesSegmentationScreeningconference paper10.1007/978-3-030-49108-6_441611-3349