  In this work, we utilize image segmentation to visually identify blood
vessels in retinal examination images. This process is typically carried out
manually. However, we can employ heuristic methods and machine learning to
automate or at least expedite the process. In this context, we propose a
cross-platform, open-source, and responsive software that allows users to
manually segment a retinal image. The purpose is to use the user-segmented
image to retrain machine learning algorithms, thereby enhancing future
automated segmentation results. Moreover, the software also incorporates and
applies certain image filters established in the literature to improve vessel
visualization. We propose the first solution of this kind in the literature.
This is the inaugural integrated software that embodies the aforementioned
attributes: open-source, responsive, and cross-platform. It offers a
comprehensive solution encompassing manual vessel segmentation, as well as the
automated execution of classification algorithms to refine predictive models.
