Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network
This work addresses the need for efficient and accurate segmentation of key retinal structures in medical imaging, which is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of automatically segmenting optic disc, fovea, and blood vessels in retinal fundus images using a single convolutional neural network, achieving an average accuracy of 92.68% on a testing set from the Drive database.
We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels. Fundus images were normalised before segmentation was performed to enforce consistency in background lighting and contrast. For every effective point in the fundus image, our algorithm extracted three channels of input from the neighbourhood of the point and forward the response across the 7 layer network. In average, our segmentation achieved an accuracy of 92.68 percent on the testing set from Drive database.