Software multiplataforma para a segmentação de vasos sanguíneos em imagens da retina
This provides a tool for medical professionals to expedite retinal image analysis, though it is incremental as it builds on existing methods.
The authors tackled the problem of manual blood vessel segmentation in retinal images by developing a cross-platform, open-source software that enables manual segmentation and uses these annotations to retrain machine learning algorithms for improved automated results.
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.