Accurate automatic segmentation of retina layers with emphasis on first layer
This work addresses the need for efficient and reliable segmentation to aid in diagnosing neurological and ocular diseases, but it appears incremental as it builds on existing methods like shortest path algorithms with extra steps.
The paper tackled the problem of automatic segmentation of retinal layers in OCT images, with a focus on accurately segmenting the first layer which is often difficult due to vanishing regions near the fovea, and reported high accuracy results.
Quantification of intra-retinal boundaries in optical coherence tomography (OCT) is a crucial task for studying and diagnosing neurological and ocular diseases. Since manual segmentation of layers is usually a time consuming task and relay on user, a lot of attempts done to do it automatically and without interference of user. Although for extracting all layers usually same procedure is applied but finding the first layer is usually more difficult due to vanishing it in some region specially close to Fobia. To have a general software, beside using common methods like applying shortest path algorithm on global gradient of image, some extra steps are used here to confine search area for Dijstra algorithm especially for the second layer. Results demonstrates high accuracy in segmenting all present layers, especially the first one that is important for diagnosing issue.