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Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema because of its non-invasive and high resolution properties. Segmentation for macular edema lesions from OCT images plays an important role in clinical diagnosis. Many computer-aided systems have been proposed for the segmentation. Most traditional segmentation methods used in these systems are based on low-level hand-crafted features, which require significant domain knowledge and are sensitive to the variations of lesions. To overcome these shortcomings, this paper proposes to use deep neural networks (DNNs) together with atrous spatial pyramid pooling (ASPP) to automatically segment the SRF and PED lesions. Lesions-related features are first extracted by DNNs, then processed by ASPP which is composed of multiple atrous convolutions with different fields of view to accommodate the various scales of the lesions. Based on ASPP, a novel module called stochastic ASPP (sASPP) is proposed to combat the co-adaptation of multiple atrous convolutions. A large OCT dataset provided by a competition platform called "AI Challenger" are used to train and evaluate the proposed model. Experimental results demonstrate that the DNNs together with ASPP achieve higher segmentation accuracy compared with the state-of-the-art method. The stochastic operation added in sASPP is empirically verified as an effective regularization method that can alleviate the overfitting problem and significantly reduce the validation error.
This article was published in the following journal.
Name: Medical image analysis
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Fluid accumulation in the outer layer of the MACULA LUTEA that results from intraocular or systemic insults. It may develop in a diffuse pattern where the macula appears thickened or it may acquire the characteristic petaloid appearance referred to as cystoid macular edema. Although macular edema may be associated with various underlying conditions, it is most commonly seen following intraocular surgery, venous occlusive disease, DIABETIC RETINOPATHY, and posterior segment inflammatory disease. (From Survey of Ophthalmology 2004; 49(5) 470-90)
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
A form of MACULAR DEGENERATION also known as dry macular degeneration marked by occurrence of a well-defined progressive lesion or atrophy in the central part of the RETINA called the MACULA LUTEA. It is distinguishable from WET MACULAR DEGENERATION in that the latter involves neovascular exudates.
An early embryonic developmental process of CHORDATES that is characterized by morphogenic movements of ECTODERM resulting in the formation of the NEURAL PLATE; the NEURAL CREST; and the NEURAL TUBE. Improper closure of the NEURAL GROOVE results in congenital NEURAL TUBE DEFECTS.
Swelling involving the deep DERMIS, subcutaneous, or submucosal tissues, representing localized EDEMA. Angioedema often occurs in the face, lips, tongue, and larynx.
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