Optic disc detection based on fully convolutional network and weighted matrix recovery model

Eye diseases often affect human health. Accurate detection of the optic disc contour is one of the important steps in diagnosing and treating eye diseases. However, the structure of fundus images is complex, and the optic disc region is often disturbed by blood vessels. Considering that the optic disc is usually a saliency region in fundus images, we propose a weakly-supervised optic disc detection method based on the fully convolution neural network (FCN) combined with the weighted low-rank matrix recovery model (WLRR). Firstly, we extract the low-level features of the fundus image and cluster the pixels using the Simple Linear Iterative Clustering (SLIC) algorithm to generate the feature matrix. Secondly, the top-down semantic prior information provided by FCN and bottom-up background prior information of the optic disc region are used to jointly construct the prior information weighting matrix, which more accurately guides the decomposition of the feature matrix into a sparse matrix representing the optic disc and a low-rank matrix representing the background. Experimental results on the DRISHTI-GS dataset and IDRiD dataset show that our method can segment the optic disc region accurately, and its performance is better than existing weakly-supervised optic disc segmentation methods.Graphical abstractGraphical abstract of optic disc segmentation

Automated summary

Eye diseases have a considerable impact on human health. This study proposes a weakly-supervised optic disc detection method based on FCN and WLRR to accurately detect the optic disc region in fundus images. The method utilizes low-level features, clustering algorithms, and prior information to accurately segment the optic disc region. Experimental results demonstrate its superior performance compared to existing weakly-supervised methods.