A coarse-to-fine deep learning framework for optic disc segmentation in fundus images

Abstract

Accurate segmentation of the optic disc (OD) depicted on color fundus images may aid in the early detection and quantitative diagnosis of retinal diseases, such as glaucoma and optic atrophy. In this study, we proposed a coarse-to-fine deep learning framework on the basis of a classical convolutional neural network (CNN), known as the U-net model, to accurately identify the optic disc. This network was trained separately on color fundus images and their grayscale vessel density maps, leading to two different segmentation results from the entire image. We combined the results using an overlap strategy to identify a local image patch (disc candidate region), which was then fed into the U-net model for further segmentation. Our experiments demonstrated that the developed framework achieved an average intersection over union (IoU) and a dice similarity coefficient (DSC) of 89.1% and 93.9%, respectively, based on 2,978 test images from our collected dataset and six public datasets, as compared to 87.4% and 92.5% obtained by only using the sole U-net model. The comparison with available approaches demonstrated a reliable and relatively high performance of the proposed deep learning framework in automated OD segmentation.

Keywords: Color fundus images; Convolutional neural networks; Image segmentation; Optic disc; U-net model.

Fig. 1.

Illustration of three fundus images with severe inhomogeneity and anomalous tissue variability

Fig. 2.

The flowchart of the proposed method for segmenting the optic disc depicted on color fundus images.

Fig. 3.

Illustration of the original fundus images and their vessel density maps. The first column corresponds to the original images, the last two columns to the vessel features and their density maps, respectively.

Fig. 4.

Example of location of local disc patches by combining two coarse segmentation results. (a) is the original fundus image, (b) is vessel density map of (a), (c) is local disc patch of (a), (d) and (e) are the segmentation results of (a) and (b), respectively, and (f) is the overlapped map of (d) and (e).

Fig. 5.

Example of the original fundus image (left) and its normalized version (right).

Fig. 6.

Illustration of segmentation results of the U-net model on different images. The first column showed three original fundus images, the last five columns from left to right were results of these fundus images, vessel density maps, 4-channel composite images, local disc patches, and their ground truths, respectively.

Fig. 7.

Illustration of the original image and its vessel density maps calculated with three different R values of 20, 40 and 60 pixels, respectively.