Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

Abstract

The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with the diagnosis of several ophthalmic diseases. For example, diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Despite contemporary advances, automatic segmentation of the choroid layer remains a challenging task due to low contrast, inhomogeneous intensity, inconsistent texture and ambiguous boundaries between the choroid and sclera in Optical Coherence Tomography (OCT) images. The majority of currently implemented methods manually or semi-automatically segment out the region of interest. While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector. This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations. The aim of this research was to segment out Bruch's Membrane (BM) and choroid layer to calculate the thickness map. BM was segmented using a series of morphological operations, whereas the choroid layer was segmented using a deep learning approach as more image statistics were required to segment accurately. Several evaluation metrics were used to test and compare the proposed method against other existing methodologies. Experimental results showed that the proposed method greatly reduced the error rate when compared with the other state-of-the-art methods.

Figure 1

The challenges being faced by the segmentation of choroid layer: (a) represents OCT image B scan with ground truth being marked by the specialist. (b) Shows the choroidal region inhomogeneous texture. (c) Illustrates the inseparable interface among the sclera and choroid layer.

Figure 2

(a) Represents a Raw B-scan of the OCT image. (b) Contains OCT image manually segmented by the experts, the image contains segmented BM and Choroid layer where BM is marked in green and choroid layer is marked in red.

Figure 3

Thickness Maps: 25 b-scans of every individual were taken into account to generate a thickness map of an individual.

Figure 4

The schematic illustration of the proposed method, which is composed of three stages: BM segmentation, Choroid layer and thickness map generation.

Figure 5

Bruch’s Membrane segmentation steps: In the diagram given above (a) represents the input image, (b) corresponds to the result of converting input image to binary format, (c) is the result thresholding operation, (d) represents the result of reconstruction (e) shows the result of thinning operation, and (f) step finally apply erosion and then spline fitting is applied to draw the final segmented BM area.

Figure 6

Steps for Choroid Layer Segmentation: Choroid layer was segmented using a series of operations, including pre-processing, data sampling, data conversion, CNN training and choroid layer segmentation.

Figure 7

Patch Sampling Process: As the choroid layer was marked by a red curve, so any patch containing a pixel on the lower line (i.e. choroid layer) was classified as on-line 0r 1 whereas any patch containing no pixel on the lower line was classified as off-line or 0. The figure shows that the patches having no pixel on boundary are labeled 0 whereas patches having any pixel on line are labeled as 1.

Figure 8

CNN Architecture: The CNN architecture being used in this research entails of a sequence of layers including convolution, rectified linear units followed by max pooling layer, fully connected and a softmax layer.

Figure 9

Overview of CNN training and choroid layer segmentation: the CNN was trained based on the extracted patches from the OCT images, test image patches were used to get the overall test accuracy of the proposed model and to obtain the overall image classification as segmentation of choroidal boundary.

Figure 10

Counter Matrix Concept: (a and b) represents the counter matrix, (c) calculate the maximum index in each column and (d) shows the segmented choroid layer after applying polynomial fitting.

Figure 11

Result of Choroid Layer Segmentation using deep learning method: (a) shows part of the image containing an OCT image being labeled by the doctor, where BM is marked in green and choroid is marked in red. (b) Represents the OCT image segmentation performed by the proposed methodology, here the choroid layer is marked in green and BM is labeled in red color.

Figure 12

Choroid layer slices to calculate Thickness Map: The thickness of each image was taken into account in order to generate the thickness map of each individual. As a result of processing each image we get a matrix representing thickness of every layer. Finally in order to get the map, matrix was resized to actual image size in order to draw the map.

Figure 13

Comparison of Mean Error rate, Variance and Standard Deviation. The Comparison of Proposed method is made with the methods k-means, Graph Cut, Graph Search and Statistical Method.

Figure 14

Dice Coefficient Similarity Results: representing the segmentation result similarity of the segmented choroid layer with the ground truth on randomly selected 25 images.

Figure 15

Thickness Map Comparison, Part (a) represents the thickness map generated by doctors segmented image where as part (b) corresponds to the thickness map generated by the proposed method.

Figure 16

Sample results of BM and Choroid layer segmentation: (a) represents sample results of BM segmentation and (b) represents sample results of Choroid layer segmentation.

Figure 17

Signed Mean Error Comparison: The Comparison in terms of Signed Mean Error of the Proposed method is done against the methods k-means, Graph Cut, Graph Search and Statistical Method.

Figure 18

Unsigned Mean Error Comparison: The Comparison in terms of Unsigned Mean Error of the Proposed method against the methods: k-means, Graph Cut, Graph Search and Statistical Method.