Automated segmentation of the macula by optical coherence tomography

Fabritius, Tapio; Makita, Shuichi; Miura, Masahiro; Myllylä, Risto; Yasuno, Yoshiaki

doi:10.1364/oe.17.015659

Cited by 105 publications

(91 citation statements)

References 11 publications

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“…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. (Fabritius et al, 2009). Likewise, Yang et al presented a fast, efficient algorithm that simultaneously utilized both local and global gradient information (Yang et al, 2010).…”

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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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 addition to segmentation software, SD-OCT systems are integrated with analysis software designed to automatically detect deviations from the RPE contour and quantify their surface area and volume [23][24][25][26][27][28][29]. Assuming classic CNV lesions form half-ellipsoid shapes; one can use such tools with automatic segmentation software in serial imaging to monitor nAMD disease progression and response to therapy ( Figure 2) [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Automatic versus Manual Segmentation of Choroidal Neovascularization Lesions Using Optical Coherence Tomography

Hossain

Moosa

Patel

2017

AOVS

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Purpose: To measure and quantify choroidal neovascularization (CNV) lesion size in classic lesions visualized on Spectral Domain-Optical Coherence Tomography (SD-OCT) using automated segmentation analysis and confirm whether further refinement improves the accuracy of disease regression after loading dose Anti-VEGF injections.Methodology: A retrospective study of 10 macular degeneration patients diagnosed with CNV. Blinded observers, trained to detect changes on OCT, delineated boundaries of CNV lesions using SD-OCT (Topcon 2000) with automated software and then modified segmentation manually before and after loading dose Ranbizumab injections. Automated measurements were then compared to manual segmentation values. Statistical analysis was performed, taking into account inter-observer variability.Results: Automatic and manual segmentation measurements are similar in 1) retinal area and fovea center values both pre-and post-injection; and 2) retinal volume post-injection. Retinal volume pre-injection is 1) similar between the two blinded observers; 2) varies between automatic measurements and observers in some patients; and 3) underestimated for all patients in the automatic measurements. Overall, automatic segmentation detects a general reduction in CNV lesion size following loading dose injections.Conclusion: SD-OCT automated segmentation software detects CNV lesions and reports size reduction after the third anti-VEGF injection. SD-OCT automatic segmentation using Topcon segmentation analysis in this small cohort is an accurate diagnostic tool to measure CNV surface area and volume as a biomarker of CNV regression.

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“…25 Tian et al presented a method to segment OCT volume data in the macular region fast and accurately using the shortest path-based graph search. 26 Fabritius et al presented a fast segmentation method for segmenting the internal limiting membrane (ILM) and the retinal pigment epithelium (RPE) that was based on variations in pixel intensity; 27 this method used A-Scan to segment 3D OCT images, where only two boundaries are detected. Niu et al proposed an algorithm to segment 3D OCT images that utilizes a customized edge°ow to produce the edge map and a convolution operator to obtain local gradient map in the axial direction.…”

Section: Introductionmentioning

confidence: 99%

3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

Sun

Zhang

Zhao

et al. 2016

J. Innov. Opt. Health Sci.

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With the introduction of spectral-domain optical coherence tomography (SD-OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of three-dimensional (3D) segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Brie°y, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing¯lter and a 3D differential¯lter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496 Â 512 (each B-Scan comprising 512 A-Scans containing 496 pixels); experimental results show that

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Automated segmentation of the macula by optical coherence tomography

Cited by 105 publications

References 11 publications

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

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

Automatic versus Manual Segmentation of Choroidal Neovascularization Lesions Using Optical Coherence Tomography

3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

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