Abstract
Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.
Highlights
Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases
We proposed a deep learning method based on the DeepLab model for the segmentation of macular edema in optical coherence tomography (OCT) images (OCT-DeepLab)
While the segmentation results by OCTDeepLab achieved higher scores of sensitivity than that of Deeplab and Deeplab + WT, and similar scores of sensitivity with that of Deeplab + FC-conditional random field (CRF)
Summary
Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. We developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCTDeepLab). OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT is a noninvasive diagnosing tool, which can enabled fast, non-invasive, high-resolution visualization of ocular structure[4,5] These are still several limitations existed in its application. Methods have been used in the segmentation of macular edema, including threshold-based, graph-based, active contours-based, and region-based a pproaches[9,10,11,12] These methods were designed based on the handcrafted features, which was highly dependent on the quality of OCT images and crafted based on domain knowledge
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