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Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images

View Article: PubMed Central - PubMed

ABSTRACT

Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world's first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM) classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR) of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.

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Images of (a) healthy eye vision, (b) RE affected vision, (c) CSCR affected vision, and (d) ARMD affected vision.
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fig1: Images of (a) healthy eye vision, (b) RE affected vision, (c) CSCR affected vision, and (d) ARMD affected vision.

Mentions: ME or RE mostly occurs due to leakage of fluid within retinal layers and leads to the formation of cyst spaces. CSCR or central serous retinopathy (CSR) occurs due to storage of serous fluid beneath neurosensory retina after intercepting retinal pigment epithelium (RPE) layer. CSCR is characterized into two stages. In type 1 CSCR, neurosensory retina remains intact while serous fluid gets accumulated in between RPE and neurosensory retina [4]. In type 2 CSCR, serous fluid breaches the RPE layer and gets accumulated within retinal pathology. ARMD highly correlates with aging and is primarily caused due to the formation of cellular debris, also known as drusen, within retinal and choroidal pathology. ARMD is mainly characterized into two types. The first form of ARMD, also known as dry ARMD, is related to the formation of drusen within the retinal and choroidal boundary that leads to the atrophy and degeneration of RPE. The second form of ARMD, also known as wet ARMD, is more severe and it is caused due to the formation of irregular blood vessels within choroid which intercepts retinal boundary causing severe visual impairments. This condition is also known as choroidal neovascularization (CNV). The symptoms of these diseases usually do not appear in early stages on fundus images. However, OCT imaging can easily detect the presence of these retinal abnormalities in early stages. These diseases can cause blurred and distorted central vision [5, 6] as shown in Figure 1.


Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images
Images of (a) healthy eye vision, (b) RE affected vision, (c) CSCR affected vision, and (d) ARMD affected vision.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382397&req=5

fig1: Images of (a) healthy eye vision, (b) RE affected vision, (c) CSCR affected vision, and (d) ARMD affected vision.
Mentions: ME or RE mostly occurs due to leakage of fluid within retinal layers and leads to the formation of cyst spaces. CSCR or central serous retinopathy (CSR) occurs due to storage of serous fluid beneath neurosensory retina after intercepting retinal pigment epithelium (RPE) layer. CSCR is characterized into two stages. In type 1 CSCR, neurosensory retina remains intact while serous fluid gets accumulated in between RPE and neurosensory retina [4]. In type 2 CSCR, serous fluid breaches the RPE layer and gets accumulated within retinal pathology. ARMD highly correlates with aging and is primarily caused due to the formation of cellular debris, also known as drusen, within retinal and choroidal pathology. ARMD is mainly characterized into two types. The first form of ARMD, also known as dry ARMD, is related to the formation of drusen within the retinal and choroidal boundary that leads to the atrophy and degeneration of RPE. The second form of ARMD, also known as wet ARMD, is more severe and it is caused due to the formation of irregular blood vessels within choroid which intercepts retinal boundary causing severe visual impairments. This condition is also known as choroidal neovascularization (CNV). The symptoms of these diseases usually do not appear in early stages on fundus images. However, OCT imaging can easily detect the presence of these retinal abnormalities in early stages. These diseases can cause blurred and distorted central vision [5, 6] as shown in Figure 1.

View Article: PubMed Central - PubMed

ABSTRACT

Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world's first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM) classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR) of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.

No MeSH data available.


Related in: MedlinePlus