As a chronic disease, age-related macular degeneration (AMD) is the leading cause of irreversible vision loss among elderly individuals, which is generally accompanied with various phenotypic manifestations.
1 The advanced stage of nonexudative AMD is generally characterized by geographic atrophy (GA) that is mainly characterized by atrophy of the retinal pigment epithelium (RPE).
2 In the comparison of AMD treatments trial, the development of GA was one of the major causes for sustained visual acuity loss,
3 which is generally associated with retinal thinning and loss of RPE and photoreceptors.
4 A recent review article notes that the reduction in the worsening of atrophy is an important biomarker for assessing the effectiveness of a given GA treatment.
5 Thus, automatic detection and characterization of retinal regions affected by GA is a fundamental and important step for clinical diagnosis, which could aid ophthalmologists in objectively measuring the regions of GA and monitor the evolution of AMD to further make treatment decisions.
6,7 GA characterization generally requires accurate segmentation. Manual segmentation is time consuming and subject to interrater variability, which may not produce reliable results especially for large data sets. Therefore, automatic, accurate, and reliable segmentation technologies are urgently needed to advanced care in AMD.
To the best of our knowledge, most semiautomated or automated image analysis methods to identify GA are applied to color fundus photographs, fundus autofluorescence (FAF), or optical coherence tomography (OCT) modalities.
8 Semiautomatic and automatic segmentation GA segmentation methods applied to these modalities can generally produce useful results and have been found to agree with manually drawn gold standards.
Color fundus photographs have been widely used for measuring GA lesions, where GA is characterized by a strongly demarcated area.
9 However, the performance of most methods mainly depends on the quality of the color fundus images. GA lesions can be easily identified in high-quality color images, while the boundaries may be more difficult to be identified in lower quality images.
As a noninvasive imaging technique for the ocular fundus, FAF can provide two-dimensional (2D) images with high contrast for the identification of GA. Both semiautomated and automated methods have been proposed for the segmentation of GA in FAF images. C. Panthier et al.
10 proposed a semiautomated image processing approach for the identification and quantification of GA on FAF images, and constructed a commercial package (i.e,. Region Finder software), which was widely used for the evaluation of GA in clinical setting. The interactive approaches including level sets,
11 watershed,
12 and region growing
13 have also been used in GA segmentation of FAF images. Meanwhile, the supervised classification methods
14 and clustering technologies
15 are widely used to automatically segment GA lesions in FAF images.
Compared with fundus imaging, spectral-domain (SD) OCT imaging technology can obtain the axial differentiation of retinal structures and additional characterization of GA.
16 Unlike the planar images provided by fundus modalities, SD-OCT can generate three-dimensional (3D) cubes composed of a set of 2D images (i.e., B-scans), and provide more detailed imaging characteristics of disease phenotypes.
17,18 Because GA is generally associated with retinal thinning and loss of the RPE and photoreceptors, earlier works mainly focused on the thickness measurement of RPE, which could be further used as the biomarkers of GA lesions.
19 However, segmenting GA is not as straightforward as solely detecting RPE. To directly identify GA lesions by characterizing RPE, state-of-the-art algorithms principally segment the GA regions based on the projection image generated with the voxels between the RPE and the choroid layers.
20–23 Chen et al.
20 used geometric active contours to produce a satisfactory performance when compared with manually defined GA regions. A level set approach was developed to segment GA regions in both SD-OCT and FAF images.
21 However, the performance of these models were generally dependent on the initializations. To further improve the segmentation accuracy and robustness to initializations, Niu et al.
22 proposed an automated GA segmentation method for SD-OCT images by using a Chan-Vese model via local similarity factor, and then used this segmentation algorithm to automatically predict the growth of GA.
23 However, as mentioned above, GA is generally associated with retinal thinning and loss of RPE and photoreceptors, and state-of-the-art algorithms mainly segment GA based on the projection image generated with the voxels between the RPE and the choroid layers, implying that these methods rely on the accuracy of retinal layer segmentation.
Recently, deep learning has gained significant success and obtained outstanding performance in many computer vision applications.
24 Much attention has been drawn to the field of computational medical imaging to investigate the potential of deep learning in medical imaging applications,
25 including medical image segmentation,
26 registration,
27 multimodal fusion,
28 diagnosis,
29 disease detection,
30 and so on. For ophthalmology applications, deep learning has also recently been applied to automated detection of diabetic retinopathy from fundus photos,
31 visual field perimetry in glaucoma patients,
32 grading of nuclear cataracts,
33 segmentation of foveal microvasculature,
34 AMD classification,
35 and identification of diabetic retinopathy.
36 Here, we use deep leaning methods to automatically discover the representations and structures inside OCT data in order to segment GA. To our best knowledge, we are the first to segment the GA lesions from OCT images with deep learning.
A deep voting model is proposed for automated GA segmentation of SD-OCT images, which is capable of achieving high segmentation accuracy without using any retinal layer segmentation results. A deep network is constructed to capture deep representations of the data, which contains five layers including one input layer, three hidden layers (sparse autoencoders; SA), and one output layer. During the training phase, the randomly selected labeled A-scans with 1024 features are directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier is trained to determine the label of each individual pixel. Finally, a voting decision strategy is used to refine the segmentation results among ten trained models. Without retinal layer segmentation, the proposed algorithm can obtain higher segmentation accuracy and is more stable compared with the state-of-the-art methods that rely on the retinal layer segmentation results. Our method can provide reliable GA segmentations from SD-OCT images and be useful for evaluating advanced nonexudative AMD.