Automated Detection of the Foveal Center Improves SD-OCT Measurements of Central Retinal Thickness

Wang, Fenghua; Gregori, Giovanni; Rosenfeld, Philip J.; Lujan, Brandon J.; Durbin, Mary K; Bagherinia, Homayoun

doi:10.3928/15428877-20121001-06

Cited by 15 publications

(18 citation statements)

References 9 publications

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“…5, it is not a very accurate measure. This is in line with previous work [17,18] and corroborates the need for an accurate automatic method for detection of the foveal center.…”

Section: Discussionsupporting

confidence: 92%

“…These values are commonly used as biomarkers in large population studies and clinical trials [15,16]. However, these measurements highly depend on a precise location of the foveal center, as deviations from the correct position have a detrimental impact in their calculated values and consequently their reliability [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…During OCT acquisition, the geometric center of the OCT volume is usually placed at the foveal center. However, this center often does not align with the actual foveal center due to the presence of pathology or poor participant's fixation [18]. Consequently, the scan center might not be a reliable estimation of the correct foveal center.…”

Section: Introductionmentioning

confidence: 99%

“…In a healthy retina, the fovea in OCT is characterized by 1) a concave dip in the retina, 2) the absence of the inner layers of the retina and 3) a slight thickening of the ellipsoid zone (EZ) [19]. In pathological retinas, the presence of abnormalities can drastically alter the expected appearance of the fovea, making the detection of the fovea a challenging problem, even for experienced human graders [18,20]. Figure 1 shows an example of a healthy and a pathological fovea.…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of the foveal center in optical coherence tomography

Liefers

Venhuizen

Schreur

et al. 2017

Biomed. Opt. Express

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We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels. For 391 scans (97.75%) the method identified the foveal center with a distance to a human reference less than 750 μm, with a mean (± SD) distance of 71 μm ± 107 μm. Two independent observers also annotated the foveal center, with a mean distance to the reference of 57 μm ± 84 μm and 56 μm ± 80 μm, respectively. Furthermore, we evaluate variations to the proposed network architecture and training procedure, providing insight in the characteristics that led to the demonstrated performance of the proposed method.

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“…5, it is not a very accurate measure. This is in line with previous work [17,18] and corroborates the need for an accurate automatic method for detection of the foveal center.…”

Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic detection of the foveal center in optical coherence tomography

Liefers

Venhuizen

Schreur

et al. 2017

Biomed. Opt. Express

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show abstract

“…Each annular region was divided in four subfields: superonasal, superotemporal, inferonasal, and inferotemporal as shown in Figure 4. For each OCT image, the location of the fovea was determined manually as described previously 31 by examining the B-scans in both the horizontal and vertical directions. The ETDRS-like grid was then centered at the corresponding foveal location on both the choroidal thickness maps and the choroidal vessel maps.…”

Section: Methodsmentioning

confidence: 99%

Choroidal Thickness and Choroidal Vessel Density in Nonexudative Age-Related Macular Degeneration Using Swept-Source Optical Coherence Tomography Imaging

Zheng

Gregori

Schaal

et al. 2016

Invest. Ophthalmol. Vis. Sci.

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PurposeTo analyze the relationship between choroidal thickness and the distribution of choroidal blood vessels in eyes with nonexudative AMD.MethodsEyes with a diagnosis of nonexudative AMD were imaged using a prototype 100-kHz swept-source (SS) optical coherence tomography (OCT) instrument (Carl Zeiss Meditec, Dublin, CA, USA) with a central wavelength of 1050 nm. We used an OCT cube scan pattern consisting of 512 × 512 A-scans over a 12 × 12 mm retinal area. The eyes were partitioned into two groups based on the presence or absence of reticular pseudodrusen (RPD). All scans were segmented using an automated algorithm. In addition, five eyes from each of the two groups were randomly chosen for manual segmentation. Binary choroidal vessels maps were generated from suitable OCT choroidal slabs, and the relationship between the density of large choroidal vessels and choroidal thickness was analyzed using an Early Treatment Diabetic Retinopathy Study–like target centered on the fovea.ResultsTwenty-five eyes were enrolled in each group. The automated algorithm produced accurate choroidal thickness maps with an average difference between the manual and automated segmentations of 13.7 μm. There was a significant and stable correlation between choroidal thickness and choroidal vessel density across the two groups. Both average choroidal thickness and vessel density were significantly lower in eyes with RPD.ConclusionsOur fully automated choroidal segmentation algorithm was able to capture the different patterns of choroidal thickness over a wide area. Choroidal thickness has a clear relationship with the density of large choroid vessels in our sample, irrespective of the presence or absence of RPD.

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Automated detection of foveal center in SD‐OCT images using the saliency of retinal thickness maps

et al. 2017

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Automated Detection of the Foveal Center Improves SD-OCT Measurements of Central Retinal Thickness

Abstract: The automated algorithm designed to detect the foveal center was more accurate in detecting the foveal center than relying on the fixation target of the SD-OCT instrument.

Cited by 15 publications

References 9 publications

Automatic detection of the foveal center in optical coherence tomography

Automatic detection of the foveal center in optical coherence tomography

Choroidal Thickness and Choroidal Vessel Density in Nonexudative Age-Related Macular Degeneration Using Swept-Source Optical Coherence Tomography Imaging

Automated detection of foveal center in SD‐OCT images using the saliency of retinal thickness maps

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