In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography

Ruggeri, Marco; Wehbe, Hassan; Jiao, Shuliang; Gregori, Giovanni; Jockovich, Maria Elena; Hackam, Abigail S.; Duan, Yuanli; Puliafito, Carmen A.

doi:10.1167/iovs.06-0815

Cited by 192 publications

(164 citation statements)

References 18 publications

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“…46 For example, Cabrera DeBuc et al developed a custom-built software for extracting up to six intraretinal layers (including RPE) from Stratus OCT images. 16,17 Several OCT prototypes with ultra-high resolution [47][48][49] have demonstrated excellent hardware setups, but image processing has lagged behind. The lagging image processing may limit the use of advanced OCT devices with higher resolution, especially at the prototype stage.…”

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography

et al. 2012

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Abstract. This study is designed to test the repeatability of the quantitative analysis of intraretinal layer thickness and cup-disc ratio of the optic nerve head using ultra-high resolution optical coherence tomography (UHR-OCT). Group A, containing 23 eyes of 12 healthy subjects, was imaged twice and group B, containing eight eyes of four subjects, was imaged three times. Intraretinal layers were segmented manually and the cup-to-disc ratio of the optic nerve head was analyzed. Custom-built automatic segmentation software was also used to segment a set of images for comparison. A total of nine intraretinal layers were visualized and extracted manually. With group A, the central foveal thickness was 186.4 AE 15.9 μm (mean AE SD). The average retinal thickness was 296.4 AE 21.3 μm. The best repeatability, obtained when two repeated scans were taken, was obtained for the outer nuclear layer followed by the ganglion cell layer, the inner nuclear layer, the retinal nerve fiber layer and the worst was obtained for the outer segment. The intraclass correlation ranged from 0.824 to 0.997. The coefficients of repeatability ranged from 3.24 to 18.3 μm, corresponding to 1.47% to 26.20%. With group B, high interclass correlations were found and the automatic segmentation results were compatible with the manual results. Our results indicated that more retinal features might be imageable using UHR-OCT.

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Section: Discussionmentioning

confidence: 99%

Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography

et al. 2012

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“…Postacquisition processing of OCT data was performed with custom programs written in MATLAB (MATLAB6.5; MathWorks, Natick, MA) as described previously (Aleman et al, 2011;Huang et al, 2012). The longitudinal reflectivity profiles (LRPs) of the OCT images were aligned manually by straightening the retinal pigment epithelium (RPE) reflection, which was defined as the second hyperreflective peak from the sclerad side (Ruggeri et al, 2007). The ONL is the hyporeflective band sclerad to the outer plexiform layer (OPL) and corresponds to the signal trough delimited by the signal peaks defining the OPL and outer limiting membrane (OLM).…”

Section: Optical Coherence Tomographymentioning

confidence: 99%

AAV-Mediated Gene Therapy in the Guanylate Cyclase (RetGC1/RetGC2) Double Knockout Mouse Model of Leber Congenital Amaurosis

et al. 2013

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Mutations in GUCY2D are associated with recessive Leber congenital amaurosis-1 (LCA1). GUCY2D encodes photoreceptor-specific, retinal guanylate cyclase-1 (RetGC1). Reports of retinal degeneration in LCA1 are conflicting; some describe no obvious degeneration and others report loss of both rods and cones. Proof of concept studies in models representing the spectrum of phenotypes is warranted. We have previously demonstrated adeno-associated virus (AAV)-mediated RetGC1 is therapeutic in GC1ko mice, a model exhibiting loss of cones only. The purpose of this study was to characterize AAV-mediated gene therapy in the RetGC1/RetGC2 double knockout (GCdko) mouse, a model lacking rod and cone function and exhibiting progressive loss of both photoreceptor subclasses. Use of this model also allowed for the evaluation of the functional efficiency of transgenic RetGC1 isozyme. Subretinal delivery of AAV8(Y733F) vector containing the human rhodopsin kinase (hGRK1) promoter driving murine Gucy2e was performed in GCdko mice at various postnatal time points. Treatment resulted in restoration of rod and cone function at all treatment ages and preservation of retinal structure in GCdko mice treated as late as 7 weeks of age. Functional gains and structural preservation were stable for at least 1 year. Treatment also conferred cortical-and subcortical-based visually-guided behavior. Functional efficiency of transgenic RetGC1 was indistinguishable from that of endogenous isozyme in congenic wild-type (WT) mice. This study clearly demonstrates AAV-mediated RetGC1 expression restores function to and preserves structure of rod and cone photoreceptors in a degenerative model of retinal guanylate cyclase deficiency, further supporting development of an AAV-based vector for treatment of LCA1.

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“…Although retinal blood vessel segmentation methods often consist of applying segmentation algorithms to fundus images, and more recently to 30 advanced OCT images (e.g. SDOCT and UHR OCT) using the vessel shadows (Wehbe et al, 2007), I consider this application of segmentation to be a separate branch of research and do not included it in this review. The segmentation methods that will be reviewed can be classified into three groups based on the dimension (D) of the image analyzed.…”

Section: Review Of Algorithms For Segmentation Of Retinal Image Data mentioning

confidence: 99%

“…Furthermore, the quantitative analysis has been largely limited to total retinal thickness and/or inner and outer retinal thickness in early studies exploring the correlation between histology and OCT in rodents (e.g. see Kocaoglu et al, 2007& Ruggeri et al, 2007. Recently, more intensity variation based approaches have also been presented (see Table 1 for details) (Fabritius et al, 2009;Tumlinson et al, 2009;Koprowski et al, 2009 ;Lu et al, 2010 andYang et al, 2010) Among them, it is worthy to mention that Fabritius et al incorporated 3D intensity information to improve the intensity based segmentation and segmented the ILM and RPE directly from the OCT data without massive pre-processing in a very faster manner.…”

Section: Review Of Algorithms For Segmentation Of Retinal Image Data mentioning

confidence: 99%

A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography

DeBuc¹

2011

Image Segmentation

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In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography

Cited by 192 publications

References 18 publications

Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography

Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography

AAV-Mediated Gene Therapy in the Guanylate Cyclase (RetGC1/RetGC2) Double Knockout Mouse Model of Leber Congenital Amaurosis

A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography

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