A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> Objective: To demonstrate a new diagnostic technique, optical coherence tomography, for high-resolution cross-sectional imaging of structures in the anterior segment of the human eye in vivo. Optical coherence tomography is a new, noninvasive, noncontact optical imaging modality that has spatial resolution superior to that of conventional clinical ultrasonography ( 90 dB). Design: Survey of intraocular structure and dimension measurements. Setting: Laboratory. Patients: Convenience sample. Main Outcome Measures: Correlation with range of accepted normal intraocular structure profiles and dimensions. Results: Direct in vivo measurements with micrometer-scale resolution were performed of corneal thickness and surface profile (including visualization of the corneal epithelium), anterior chamber depth and angle, and iris thickness and surface profile. Dense nuclear cataracts were successfully imaged through their full thickness in a cold cataract model in calf eyes in vitro. Conclusions: Optical coherence tomography has potential as a diagnostic tool for applications in noncontact biometry, anterior chamber angle assessment, identification and monitoring of intraocular masses and tumors, and elucidation of abnormalities of the cornea, iris, and crystalline lens. <s> BIB001 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> Objective: To demonstrate optical coherence tomography for high-resolution, noninvasive imaging of the human retina. Optical coherence tomography is a new imaging technique analogous to ultrasound B scan that can provide cross-sectional images of the retina with micrometer-scale resolution. Design: Survey optical coherence tomographic examination of the retina, including the macula and optic nerve head in normal human subjects. Settings Research laboratory. Participants: Convenience sample of normal human subjects. Main Outcome Measures: Correlation of optical coherence retinal tomographs with known normal retinal anatomy. Results: Optical coherence tomographs can discriminate the cross-sectional morphologic features of the fovea and optic disc, the layered structure of the retina, and normal anatomic variations in retinal and retinal nerve fiber layer thicknesses with 10- μm depth resolution. Conclusion: Optical coherence tomography is a potentially useful technique for high depth resolution, cross-sectional examination of the fundus. <s> BIB002 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> Background Optical coherence tomography (OCT) is a recently developed medical diagnostic technology that uses back-reflected infrared light to perform in situ micron scale tomographic imaging. In this work, we investigate the ability of OCT to perform micron scale tomographic imaging of the internal microstructure of in vitro atherosclerotic plaques. Methods and Results Aorta and relevant nonvascular tissue were obtained at autopsy. Two-dimensional cross-sectional imaging of the exposed surface of the arterial segments was performed in vitro with OCT. A 1300-nm wavelength, superluminescent diode light source was used that allows an axial spatial resolution of 20 μm. The signal-to-noise ratio was 109 dB. Images were displayed in gray scale or false color. Imaging was performed over 1.5 mm into heavily calcified tissue, and a high contrast was noted between lipid- and water-based constituents. making OCT attractive for intracoronary imaging. The 20-μm axial resolution of OCT allowed small structural details such as the width of intimal caps and the presence of fissures to be determined. The extent of lipid collections, which had a low backscattering intensity, also were well documented. Conclusions OCT represents a promising new technology for imaging vascular microstructure with a level of resolution not previously achieved with the use of other imaging modalities. It does not require direct contact with the vessel wall and can be performed with a catheter integrated with a relatively inexpensive optical fiber. The high contrast among tissue constituents, high resolution, and ability to penetrate heavily calcified tissue make OCT an attractive new imaging technology for intracoronary diagnostics. <s> BIB003 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> We present theoretical and experimental results which demonstrate the superior sensitivity of swept source (SS) and Fourier domain (FD) optical coherence tomography (OCT) techniques over the conventional time domain (TD) approach. We show that SS- and FD-OCT have equivalent expressions for system signal-to-noise ratio which result in a typical sensitivity advantage of 20-30dB over TD-OCT. Experimental verification is provided using two novel spectral discrimination (SD) OCT systems: a differential fiber-based 800nm FD-OCT system which employs deep-well photodiode arrays, and a differential 1300nm SS-OCT system based on a swept laser with an 87nm tuning range. <s> BIB004 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> Objective Optical coherence tomography (OCT) has been shown to be a valuable tool in glaucoma assessment. We investigated a new ultrahigh-resolution OCT (UHR-OCT) imaging system in glaucoma patients and compared the findings with those obtained by conventional-resolution OCT. Design Retrospective comparative case series. Participants A normal subject and 4 glaucoma patients representing various stages of glaucomatous damage. Testing All participants were scanned with StratusOCT (axial resolution of ∼10 μm) and UHR-OCT (axial resolution of ∼3 μm) at the same visit. Main outcome measure Comparison of OCT findings detected with StratusOCT and UHR-OCT. Results Ultrahigh-resolution OCT provides a detailed cross-sectional view of the scanned retinal area that allows differentiation between retinal layers. These UHR images were markedly better than those obtained by the conventional-resolution OCT. Conclusions Ultrahigh-resolution OCT provides high-resolution images of the ocular posterior segment, which improves the ability to detect retinal abnormalities due to glaucoma. <s> BIB005 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> BACKGROUND ::: We differentiated between normal and glaucomatous eyes in the Taiwan Chinese population based solely on the quantitative assessment of summary data reports from Stratus optical coherence tomography (OCT) by comparing their area under the receiver operating characteristic (ROC) curve. ::: ::: ::: METHODS ::: One randomly selected eye from each of the 62 patients with early glaucomatous damage (mean deviation -2.8 +/- 1.8 dB) and from each of the 62 age- and sex-matched normal individuals were included in the study. Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head analysis results) were obtained by Stratus OCT. Twenty-one OCT parameters were included in a linear discriminant analysis (LDA) using forward selection and backward elimination to determine the best combination of parameters for discriminating between glaucomatous and healthy eyes based on ROC curve area. ::: ::: ::: RESULTS ::: The average RNFL thickness was the best individual parameter for differentiating between normal eyes and glaucomatous eyes (ROC curve area 0.793). The maximum area under the ROC curve of six input parameters (average RNFL thickness; 10, 11, and 12 o'clock segment thicknesses; cup area; and vertical integrated rim area) generated by the forward selection method was 0.881. Whereas the maximum area under the ROC curve of 15 input parameters (average RNFL thickness; 1, 3, 4, 6, 8-10, 12 o'clock segment thicknesses; vertical integrated rim area; horizontal integrated rim area; disc area; cup to disc area ratio; cup to disc horizontal ratio; and cup to disc vertical ratio) generated by backward elimination method was 0.929. ::: ::: ::: CONCLUSIONS ::: The performance of individual parameters obtained from Stratus OCT is fairly reliable for differentiating the early glaucomatous eyes from normal eyes. However, the discriminant power increases when LDA with forward selection and backward elimination methods is applied. <s> BIB006 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> PURPOSE ::: To develop a software algorithm to perform automated segmentation of retinal layer structures on linear macular optical coherence tomography (StratusOCT; Carl Zeiss Meditec, Inc., Dublin, CA) scan images and to test its performance in discriminating normal from glaucomatous eyes in comparison with conventional circumpapillary nerve fiber layer (cpNFL) thickness measurement. ::: ::: ::: METHODS ::: Four layer structures within the retina were defined: the macular nerve fiber layer (mNFL), the inner retinal complex (IRC; retinal ganglion cell [RGC] layer + inner plexiform and nuclear layers), outer plexiform layer (OPL), and outer retinal complex (ORC; outer nuclear layer + photoreceptor layer). Normal and glaucomatous eyes underwent fast macular map and fast NFL OCT scans. Linear macular images were analyzed using the developed algorithm, and the results were compared with the cpNFL thickness measurement. ::: ::: ::: RESULTS ::: Forty-seven subjects (23 normal and 24 with glaucoma) were analyzed. mNFL, cpNFL, IRC, and the total retinal thicknesses were significantly greater in normal than in glaucomatous eyes (P < or = 0.0002; Wilcoxon), whereas OPL thickness did not show a significant difference (P = 0.46). ORC thickness was significantly greater in glaucomatous than normal eyes (P = 0.035). Areas under the receiver operator characteristic curve (AROCs) for discriminating normal from glaucomatous eyes were highest with mNFL + IRC (0.97) and lowest with OPL (0.56). AROCs for OPL and ORC were significantly smaller than those for mNFL, IRC, mNFL+IRC, and cpNFL (P < or = 0.01). AROCs for IRC, mNFL + IRC, and cpNFL were significantly larger than for retinal thickness (P < or = 0.049). Among the best-performing parameters (mNFL, IRC, mNFL + IRC, and cpNFL) there was no significant difference in AROCs (P > or = 0.15). ::: ::: ::: CONCLUSIONS ::: The newly developed macular segmentation algorithm described herein demonstrated its ability to quantify objectively the glaucomatous damage to RGCs and NFL and to discriminate between glaucomatous and normal eyes. Further algorithm refinement and improvements in resolution and image quality may yield a more powerful methodology for clinical glaucoma evaluation. <s> BIB007 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> Purpose To compare the ability of optical coherence tomography retinal nerve fiber layer (RNFL), optic nerve head, and macular thickness parameters to differentiate between healthy eyes and eyes with glaucomatous visual field loss. Design Observational case-control study. Methods Eighty-eight patients with glaucoma and 78 healthy subjects were included. All patients underwent ONH, RNFL thickness, and macular thickness scans with Stratus OCT during the same visit. ROC curves and sensitivities at fixed specificities were calculated for each parameter. A discriminant analysis was performed to develop a linear discriminant function designed to identify and combine the best parameters. This LDF was subsequently tested on an independent sample consisting of 63 eyes of 63 subjects (27 glaucomatous and 36 healthy individuals) from a different geographic area. Results No statistically significant difference was found between the areas under the ROC curves (AUC) for the RNFL thickness parameter with the largest AUC (inferior thickness, AUC=0.91) and the ONH parameter with largest AUC (cup/disk area ratio, AUC=0.88) ( P = .28). The RNFL parameter inferior thickness had a significantly larger AUC than the macular thickness parameter with largest AUC (inferior outer macular thickness, AUC=0.81) ( P = .004). A combination of selected RNFL and ONH parameters resulted in the best classification function for glaucoma detection with an AUC of 0.97 when applied to the independent sample. Conclusions RNFL and ONH measurements had the best discriminating performance among the several Stratus OCT parameters. A combination of ONH and RNFL parameters improved the diagnostic accuracy for glaucoma detection using this instrument. <s> BIB008 </s> A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina <s> principles of opTical coherence Tomography <s> PURPOSE ::: To compare the diagnostic ability of the confocal scanning laser ophthalmoscope (HRT-II; Heidelberg Engineering, Heidelberg, Germany), scanning laser polarimeter (GDx-VCC; Carl Zeiss Meditec, Inc., Dublin, CA), and optical coherence tomographer (StratusOCT, Carl Zeiss Meditec, Inc.) with subjective assessment of optic nerve head (ONH) stereophotographs in discriminating glaucomatous from nonglaucomatous eyes. ::: ::: ::: METHODS ::: Data from 79 glaucomatous and 149 normal eyes of 228 subjects were included in the analysis. Three independent graders evaluated ONH stereophotographs. Receiver operating characteristic curves were constructed for each technique and sensitivity was estimated at 80% of specificity. Comparisons of areas under these curves (aROC) and agreement (kappa) were determined between stereophoto grading and best parameter from each technique. ::: ::: ::: RESULTS ::: Stereophotograph grading had the largest aROC and sensitivity (0.903, 77.22%) in comparison with the best parameter from each technique: HRT-II global cup-to-disc area ratio (0.861, 75.95%); GDx-VCC Nerve Fiber Indicator (NFI; 0.836, 68.35%); and StratusOCT retinal nerve fiber layer (RNFL) thickness (0.844, 69.62%), ONH vertical integrated rim area (VIRA; 0.854, 73.42%), and macular thickness (0.815, 67.09%). The kappa between photograph grading and imaging parameters was 0.71 for StratusOCT-VIRA, 0.57 for HRT-II cup-to-disc area ratio, 0.51 for GDX-VCC NFI, 0.33 for StratusOCT RNFL, and 0.28 for StratusOCT macular thickness. ::: ::: ::: CONCLUSIONS ::: Similar diagnostic ability was found for all imaging techniques, but none demonstrated superiority to subjective assessment of the ONH. Agreement between disease classification with subjective assessment of ONH and imaging techniques was greater for techniques that evaluate ONH topography than with techniques that evaluate RNFL parameters. A combination of subjective ONH evaluation with RNFL parameters provides additive information, may have clinical impact, and deserves to be considered in the design of future studies comparing objective techniques with subjective evaluation by general eye care providers. <s> BIB009
Optical coherence tomography utilizes near infrared super luminescent diode light in a fashion similar to the way B-mode ultrasound uses sound to generate two-dimensional images. Although sound has the advantage of penetrating tissue (for example, scanning a fetus in utero), light, with its much shorter wavelength, has the advantage of obtaining significantly higher resolution.A simplified optical setup for a fiber based OCT system utilizing a low coherence source and a Michelson-type interferometer is illustrated in Figure 1 . Cross sectional data along an axial line through the sample, called an A-scan, is acquired by axially scanning the position of the reference arm. Interference fringes are acquired at the photodiode detector when the round trip distance from the sample reflection matches that of the reference reflection. The magnitude of the observed fringes is proportional to the reflectivity of the scatterer. A two dimensional profile, or B-scan, is generated by scanning the interrogating beam laterally across the sample and acquiring an axial scan at each lateral location. Subtle differences in adjacent layers are visualized as differences in scattering intensities.  BIB002  BIB001 The scan speed of the time-domain OCT scanner such as a Stratus OCT (Carl Zeiss Meditec, Inc., Dublin, CA, USA) machine is 400 A-scans/sec. The scan speed is limited by the speed at which the reference mirror can be moved. Therefore, the time-domain OCT scanner can acquire only several cross-sectional images (typically 6 × 128 × 1024 voxels). The slow scan speed causes a bad effect on image quality such as eye movement artifacts [ Figure 2a ]. The voxel size is 30 × 30 × 2 μm, the voxel depth is 8 bits in gray scale, and the acquisition time for each scan consisting of 6 × 128 × 1024 voxels is 1.92 seconds.  In time domain OCT the location of scatters in the sample is observed by generation of interferometric fringes at the detector as the reference reflector position is axially translated. In contrast, Fourier domain OCT required the reference arm to be held fixed, and the optical path length difference between sample and reference reflections is encoded by the frequency of the interferometric fringes as  b a consisting of 200 × 200 × 1024 voxels is 1.48 seconds. The image quality of the spectral-domain OCT is much better than that of the time-domain OCT [ Figure 2b ].  The spectral-domain OCT scanner can provide true 3-D views of the retinal structure. Two kinds of OCT scans, the scan centered at the fovea (macular scan) related to central vision and the scan centered at the optic nerve head (ONH-centered scan) related to peripheral vision, are mainly acquired to examine patients' eyes [ Figure 5 ].  The layers of The reTina Covering the inside of most of the eye [ Figure 6 ], the retina is a multilayered structure responsible for transforming light energy into neural signals for further use by the brain. In very general terms, the processing of light starts with the light sensitive photoreceptor cells (rods and cones), which are actually located in the outer portion of the retina (away from the incoming light).  These cells convert the light signal into action potentials that are transmitted by the bipolar neurons in the central layers of the retina to the ganglion cells of the inner retina. It is the axons of the ganglion cells that eventually exit the eye to form the optic nerve.  Other cells in the retina, such as horizontal cells, amacrine cells and interplexiform neurons, also help in the processing of the neural signal at a local level. Neuroglial cells (such as Muller cells) provide structure and support.  Many of the cells of the retina are illustrated in Figure 6a . Based on its appearance from light microscopy [ Figure 6b ], the retina is traditionallyconsidered to be composed of the following ten major "layers" (starting with the outermost layer):  •  a function of the source spectrum. Two configurations have prevailed in Fourier domain systems: spectral domain (SD) OCT uses a grating (network of parallel wires) to spatially disperse the spectrum across an array-type detector, and in swept source (SS) OCT a narrow band laser is swept across a broad spectrum, encoding the spectrum as a function of time [ Figure 3 ]. BIB002  BIB001 BIB003 Regardless of whether the spectrum is sampled in time or in position across an array detector, the frequency of the interferometric fringes as a function of spectrum encodes the location of the scatter, with increasing frequency corresponding to larger optical path length mismatches. A common mathematical tool for extracting the frequency content of a signal is the Fourier transform, however, it must be remembered that the true Fourier transform pair of distance is not wavelength (also units of distance), but in fact wavenumber (with units of inverse distance). BIB004 The scan speed of the spectral-domain OCT scanner such as a Cirrus HD-OCT (Carl Zeiss Meditec, Inc., Dublin, CA, USA) machine is 27,000 A-scans/sec. The Cirrus HD-OCT scanner can acquire a retinal volume whose dimension is typically 200 × 200 × 1024 voxels covering 6 × 6 × 2 mm  [ Figure 4 ]. The voxel size is 30 × 30 × 2 μm, the voxel depth is 8 bits in grayscale, and the acquisition time for each volumetric scan Intraretinal layers are also visible from optical coherence tomography images as shown in the example image of [ Figure 6c and d]. Note that the precise anatomical correspondence of the layers visible in OCT images is not known. The anatomical labels are our current presumption based on comparisons with histology and images from higher resolution OCT scanners published in the literature. BIB005 clinical applicaTions of ocT segmenTaTion The most motivating point in clinical applications of OCT segmentation is providing a quantitative tool to help the ophthalmologists to manage the high complexity of the OCT data and to create a tool for better observation of different boundaries and individual layers. There is no doubt that in new modalities of OCT, the 3D data is overly abundant which makes the interpretation really difficult for the observer; but, a correct segmentation can provide useful information about the layers and makes the ophthalmologist able to perform better diagnosis and treatment. A variety of applications may be defined with this approach which can be categorized as: • Earlier detection of ganglion cell loss in cases of concurrent optic nerve swelling can be possible. In cases of optic disc edema, it is possible that information in the macular scans, such as the thickness of the ganglion cell layer from an intraretinal segmentation, be able to detect such loss at an earlier stage  • The thinning of the RNFL is an important marker used by ophthalmologists in the diagnosis of the disease. It has also been used to differentiate between glaucomatous and normal eyes BIB009 BIB006 BIB007 BIB008 • Volumetric measurements in the whole scan, within selected sectors/quadrants and radial distances can be useful in detection of retinal pathologies • The comparison of retinal thickness during the treatment can become more simple and quantitative using intraretinal segmentation • Calculation of similarities between right and left eye can be assisted using intraretinal segmentation and such similarities seem to be a promising sign of healthiness in individuals • Different pathologies may make particular layers thinner (or even eliminates them), which can be found through layer segmentation approaches.