Macular Segmentation with Optical Coherence Tomography

Ishikawa, Hiroshi; Stein, Daniel M.; Wollstein, Gadi; Beaton, S.; Fujimoto, James G.; Schuman, Joel S.

doi:10.1167/iovs.04-0335

Cited by 472 publications

(407 citation statements)

References 10 publications

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“…Dendritic field sizes were significantly reduced in RGC of both hypertensive and diabetic retinas. 20 Our findings also showed that there was no significant correlation between GCC thickness and hypertension and diabetes.…”

Section: Discussionsupporting

confidence: 54%

Ganglion cell complex thickness in nonexudative age-related macular degeneration

Yenice

Şengün

Demirok

et al. 2015

Eye

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Purpose To evaluate ganglion cell complex (GCC) thickness with spectral domain optical coherence tomography (SD-OCT) in eyes with nonexudative age-related macular degeneration (NEAMD). Methods Forty-seven eyes of 28 patients with nonexudative age-related macular degeneration (NEAMD) and 54 eyes of 28 age-matched healthy subjects were enrolled. Each subject underwent a complete ophthalmic examination before SD-OCT were obtained. Macular scans were taken with software version 6.0 of the ganglion cell analysis (GCA) algorithm. GCC thickness was evaluated automatically as the average, minimum, temporal superior, superior, nasal superior, nasal inferior, inferior, and temporal-inferior segments by SD-OCT and parameters were compared between groups. Results The mean age was 68.7 ± 8.73 years in patient group, and 61.51 ± 5.66 years in control group. There were no significant differences in mean age, gender distribution, intraocular pressure, and sferic equivalent at imaging between the groups (P40.05). The mean (± SD) GCC thicknesses were as follows; average 71.53 ±16.53 μm, minumum 62.36 ± 21.51 μm, temporal superior 72.23 ± 14.60 μm, superior 72.76 ± 20.40 μm, nasal superior 72.31 ± 20.13 μm, nasal inferior 69.74 ± 20.51 μm, inferior 69.38 ± 19.03 μm, and temporal-inferior 73.12 ± 15.44 μm in patient group. Corresponding values in control group were 81.46 ± 4.90 μm, 78.66 ± 6.00 μm, 81.51 ± 4.66 μm, 82.94 ± 5.14 μm, 81.79 ± 5.86 μm, 80.94 ± 6.18 μm, 80.14 ± 6.30 μm, and 81.75 ± 5.26 μm, respectively. There were significant differences between two groups in each segments (Mann-Whitney U-test, Po0.05). Conclusion The average GCC thickness values (in all segments) of NEAMD patients were lower than control group. NEAMD, which is considered as a disease of outer layers of retina, may be accompanied with a decrease of ganglion cell thickness, so inner layers of retina may be affected.

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Section: Discussionsupporting

confidence: 54%

Ganglion cell complex thickness in nonexudative age-related macular degeneration

Yenice

Şengün

Demirok

et al. 2015

Eye

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“…Such studies were conducted based on the theory that the macular thickness might be superior to circumpapillary RNFL for quantification of axonal loss, because retinal ganglion cell bodies are 10 to 20 times the diameter of their axons and because the retinal ganglion cell layer is more than one cell thick in the macula (25) . However, despite the confirmation that macular thickness is in fact reduced in patients with glaucoma, several studies have reported circumpapillary RNFL thickness to be a more sensitive detector of glaucoma than macular thickness (19,23,(26)(27) .…”

Section: Discussionmentioning

confidence: 99%

“…In this context, it should be recalled that glaucoma and other optic neuropathies affect only the inner retinal layers whereas the outer layers merely increase measurement variability (28) . On the other hand, technological improve- ments in image resolution have made segmentation and measurement of individual retinal layers possible improving the reliability of macular thickness measurements when compared to RNFL thickness measurements for detecting axonal loss in glaucoma (25,28) . In the present study, macular thickness parameters were confirmed to be significantly lower in eyes with BA of the optic nerve than in healthy control eyes (11) (Table 1).…”

Section: Discussionmentioning

confidence: 99%

Relationship between visual field sensitivity loss and quadrantic macular thickness measured with Stratus-Optical coherence tomography in patients with chiasmal syndrome

Moura¹,

Costa-Cunha²,

Malta³

et al. 2010

Arq. Bras. Oftalmol.

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Purpose: To correlate visual field sensitivity (VFS) loss on standard automated perimetry (SAP) and quadrantic macular thickness on optical coherence tomography (OCT) in patients with permanent temporal hemianopia from chiasmal compression. Methods: Forty eyes from 40 patients with chiasmal compression and 40 healthy eyes were submitted to standard automated perimetry and Stratus-OCT scanning. Raw data of the fast macular thickness scanning protocol were exported and macular thickness measurements were recorded and averaged for each quadrant and half of the central area. The correlation between visual field sensitivity loss and optical coherence tomography measurements was tested with Pearson's correlation coefficients and with linear regression analysis. Results: A significant association was found between each macular thickness parameter and the corresponding central VF mean sensitivity. The strongest association was observed between superonasal macular thickness and the inferotemporal mean defect measured both in decibel (R=0.47; p=0.001) and in 1/Lambert (R=0.59; p<0.0001) units. Conclusion: Stratus-OCT-measured macular thickness was topographically related with visual field sensitivity loss in patients with temporal hemianopia from chiasmal compression. Such measurements could prove clinically useful in the diagnosis and follow-up of patients with chiasmal compression. ClinicalTrial.gov identifier number: NCT0039122.Keywords: Tomography, optical coherence; Perimetry; Optic atrophy; Optic chiasm; Hemianopsia; Visual fields RESUMO Objetivo: Avaliar a correlação entre o defeito de campo visual ao exame de perimetria computadorizada e a espessura macular quadrântica ao exame de tomografia de coerência óptica (OCT) em pacientes com hemianopsia temporal permanente causada por compressão quiasmática. Métodos: Quarenta olhos de 40 pacientes com compressão quiasmática e 40 olhos de 40 indivíduos controles foram submetidos aos exames de perimetria computadorizada e tomografia de coerência óptica. Dados não processados foram exportados e as medidas de espessura macular foram calculadas para cada quadrante e metade da área macular central. A correlação entre o defeito campimétrico e as medidas de espessura macular foi avaliada por coeficiente de correlação de Pearson e por análise de regressão linear. Resultados: Associação significante foi encontrada entre os parâmetros de espessura macular e seus respectivos defeitos campimétricos. A correlação mais forte foi encontrada entre o parâmetro espessura macular nasal superior e o defeito campimétrico médio temporal inferior medido em decibel (R=0,47; p=0,001) e em 1/Lambert (R=0,59; p<0,0001). Conclusão: Medidas de espessura macular avaliada através da tomografia de coerência óptica foi topograficamente relacionada ao defeito campimétrico em pacientes com hemianopsia temporal por compressão quiasmática. Estas medidas podem provar a importância clínica no diagnóstico e seguimento dos pacientes com compressão quiasmática. ClinicalTrial.gov identifier number: NCT00391...

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“…Mathematic model based methods construct a fixed or adaptive model based on prior assumptions for the structure of the input images, and include A-scan [16,17], active contour [18][19][20][21], sparse high order potentials [22], and 2D/3D graph [23][24][25][26][27][28][29][30] based methods. Machine learning based methods formulate layer segmentation as a classification problem, where features are extracted from each layer or its boundaries and used to train a classifier (e.g.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

Fang¹,

Cunefare²,

Wang³

et al. 2017

Biomed. Opt. Express

438

283

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Abstract:We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique. 1178-1181 (1991). 2. S. Bhat, I. V. Larina, K. V. Larin, M. E. Dickinson, and M. Liebling, "4D reconstruction of the beating embryonic heart from two orthogonal sets of parallel optical coherence tomography slice-sequences," IEEE Trans. Med. Imaging 32(3), 578-588 (2013). 3. P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, "Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration," Invest. Ophthalmol. Vis. Sci. 50(7), 3378-3385 (2009). 4. P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, "Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative agerelated macular degeneration," Retina 31(3), 453-463 (2011). 5. P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images," Biomed. Opt. Express 5(10), 3568-3577 (2014). 6. J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W.Gardner, "The effects of diabetic retinopathy and pan-retinal photocoagulation on photoreceptor cell function as assessed by dark adaptometry DR and PRP effects on photoreceptor cell function," Invest. Ophthalmol. Vis. Sci. 57(1), 208-217 (2016). 7. C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G.Fujimoto, "Imaging of macular diseases with optical coherence tomography," Ophthalmology 102(2), 217-229 (1995

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Macular Segmentation with Optical Coherence Tomography

Cited by 472 publications

References 10 publications

Ganglion cell complex thickness in nonexudative age-related macular degeneration

Ganglion cell complex thickness in nonexudative age-related macular degeneration

Relationship between visual field sensitivity loss and quadrantic macular thickness measured with Stratus-Optical coherence tomography in patients with chiasmal syndrome

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

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