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From the Doheny Image Reading Center, Doheny Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California.
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Abstract |
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METHODS. The raw exported StratusOCT (Carl Zeiss Meditec, Inc., Dublin, CA) scan data from 20 eyes of 20 patients were analyzed using custom software (termed OCTOR) designed to allow the user to define manually the retinal borders on each radial line scan. Measurements calculated by the software, including thickness of the nine standard macular subfields, foveal center point (FCP), and macular volume, were compared between two graders and with the automated Stratus analysis. Mean and range of differences for each parameter were calculated and assessed by Bland-Altman plots and Pearson correlation coefficients. Additional cases with clinically relevant subretinal findings were selected to demonstrate the capabilities of this system for quantitative feature subanalysis.
RESULTS. Retinal thickness measurements for the various subfields and the FCP showed a mean difference of 1.7 µm (maximum, 7 µm) between OCTOR graders and a mean difference of 2.3 µm (maximum of 8 µm) between the OCTOR and Stratus analysis methods. Volume measurements between Stratus and OCTOR methods differed by a mean of 0.06 mm3 (in reference to a mean macular volume of 6.81 mm3). The differences were not statistically significant, and the thicknesses correlated highly (R2 
0.98 for all parameters).
CONCLUSIONS. Manual identification of the inner and outer retinal boundaries on OCT scans can produce retinal thickness measurements consistent with those derived from the automated StratusOCT analysis. Computer-assisted OCT grading may be useful for correcting thickness measurements in cases with errors of automated retinal boundary detection and may be useful for quantitative subanalysis of clinically relevant features, such as subretinal fluid volume or pigment epithelial detachment volume.
Several investigators have demonstrated a relatively high reproducibility of OCT measurements,22 23 24 25 26 27 which has increased clinician confidence in the use of these measures in follow-up of patients with macular disease. Artifacts in the OCT scans, however, can have a significant impact on the accuracy of these measurements.28 29 30 31 Ray et al.31 observed that artifacts affecting retinal thickness were present in 43.2% of cases. Using stringent criteria, we had noted errors in the detection of retinal boundaries in 92% of cases, with moderate errors in 19.5% and severe errors in 13.5%, in a series of 200 patients who underwent StratusOCT imaging with the version 4.0 software (Carl Zeiss Meditec, Inc., Dublin, CA).32 Moreover, in patients with CNV, we observed more frequent and severe OCT retinal boundary detection errors than in patients with other macular diseases.
The best solution to this problem is the development of improved segmentation algorithms for more consistently accurate detection of retinal boundaries, but such algorithms are not yet available. In the interim, strategies that allow a trained human expert to correct OCT errors may provide a suitable mechanism for increasing the precision of retinal thickness measurements for monitoring patients with macular disease, particularly in clinical trials.
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Methods |
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The resultant pseudocolor B-scan images were manually inspected by a certified Doheny Image Reading Center (DIRC) grader (SRS or ZW) until 20 consecutive cases were found that had accurate automated detection by the StratusOCT software of the inner and outer retinal boundaries in all six radial line scans. Accrual of this 20-case cohort required review of 150 consecutive cases from the Imaging Unit log. These selected "accurately detected" scans were frequently the fellow healthy eye of patients with a diseased eye.
The inner retinal boundary was deemed to be properly positioned when it coincided with the anticipated location of the internal limiting membrane (ILM). The outer retinal boundary was determined to be correctly positioned when it coincided with the anterior aspect of the highly reflective band previously believed to correspond to the RPE-choriocapillaris interface. Additional cases with notable boundary detection errors or clinically relevant subretinal features that were improperly outlined by the automated segmentation software were also collected, for demonstration purposes only.
Data for each case were exported to disc with the export feature available in the StratusOCT version 4.0 analysis software.
Computer-Assisted OCT Grading Software
Software (termed OCTOR) was written by DIRC software engineers to facilitate viewing and manual grading of each of the radial line scans by a trained OCT grader. The OCTOR software is not commercially available, but a web-based application (www.driamd.org) is being developed to permit generalized use of this technique. The software, which effectively operates as a painting program and calculator, imports data exported from the StratusOCT machine and allows the grader to use a computer mouse to draw various boundaries in the retinal cross-sectional image, including: the inner retinal surface, the outer retinal boundary, the RPE-choriocapillaris interface (if different from the outer retinal boundary, as in cases with subretinal fluid), and the estimated level of the normal RPE (if different from the RPE-choriocapillaris interface, as in cases of pigment epithelial detachment). The software allows the user to select one boundary line to be drawn at a time, from a list of the previously described interfaces. The OCTOR software also allows the user to zoom and pan across the image, thereby facilitating rapid and accurate drawing of the boundaries, by simply moving the computer mouse pointer from one end of the B-scan to the other. The user is also able to correct mistakes, such as a boundary drawn in the wrong location, rapidly by simply redrawing the line in the correct location. The OCTOR software automatically erases the previously drawn lines in those transverse locations that have been redrawn. After drawing the boundary, the grader scrutinizes the drawn line to make sure there are no discontinuities. An illustration of the appearance of a B-scan on the computer screen after the boundaries are partially drawn (using a computer mouse) by the user is shown in Figure 1 . Through the use of the zoom feature, we have found the simple manual drawing method to be faster and more reliable than the use of anchor points and spline interpolation. No smoothing or other processing of the manually drawn line is performed. The grader then proceeds to draw the desired boundaries in each of the six B-scans.
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To allow direct comparison to the StratusOCT output, the OCTOR software also calculates the average retinal thickness in each of the nine Early Treatment Diabetic Retinopathy Study (ETDRS) macular subfields, the mean and SD of the foveal center point (FCP) thickness, and the total macular volume (by multiplying the average thickness by the sampled area). The FCP and the foveal central subfield (FCS) thickness are the two most common StratusOCT output parameters used by clinicians and clinical trial reading centers.
Computer-Assisted Grading Protocol
Two independent, masked DIRC graders (SJ and ZW) used the OCTOR software in all cases to draw lines (using the computer mouse) representing the inner and outer retinal boundaries on the six raw, exported, radial line scans. For each grader, the manual-grading process took approximately 5 minutes for each set of six scans. After completion of the grading, each grader used the OCTOR software to calculate the retinal thicknesses for the various standard subfields, FCP, and macular volume, to allow comparison between graders to assess the reproducibility of the OCTOR analysis method. In addition, the retinal thickness values for the two graders were averaged for subsequent comparison with the automated StratusOCT output. Finally, one grader (SJ) regraded and analyzed all the cases several weeks after the initial assessment to provide data regarding the intragrader reproducibility of the method.
For the primary analysis, the graders drew the line for the inner retinal boundary at the inner edge of the internal limiting membrane. To allow a fair comparison to the automated results, the graders adopted the convention used by the StratusOCT algorithms, and drew the outer retinal boundary at the inner edge of the hyperreflective band between the retina and the choroid. The true outer retinal boundary may lie between "layers" of the hyperreflective band that can be easily discerned on high-quality scans or in patients with a healthy RPE, as reported by Costa et al.33 and Pons and Garcia-Valenzuela,29 and illustrated by the case in Figure 2 . Consequently, to quantify the difference between retinal thickness measurements from the StratusOCT software and measurements (called "true retinal thickness") that incorporate this recent understanding of the outer retinal boundary, cases were manually regraded by using this new interface as the outer retinal boundary. Automated Stratus retinal thickness measurements were compared to the calculated "true" thicknesses for each A-scan.
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As a final method of comparison, in addition to comparing the 11 outcome parameters (i.e., the clinically reported information), the retinal thickness measurements at the individual points (each of the 512 A-scans) along all six scan lines were averaged to yield a mean "raw" (uninterpolated) retinal thickness for each case. The mean absolute difference in retinal thicknesses between the two graders was calculated for all 20 cases. A similar analysis was also performed to compare OCTOR measurements obtained using the two different definitions of the outer retinal boundary.
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Results |
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Comparison between Computer-Assisted Manual Grading and Automated Stratus Measurements
Table 4 shows the thickness measurements obtained by the computer-assisted OCTOR (average of the two graders) and automated Stratus analyses. Pearson correlation coefficients between automated and manual values demonstrated R2 0.98 for all 11 parameters. The mean difference between the FCP thickness obtained by the Stratus analysis and the computer-assisted human assessment was 3.7 µm (2.4%), with a median of 3.3 µm, and a maximum of 7.5 µm (5.9%). For the FCS thickness, the mean difference was 2.0 µm (1.0%) with a median of 2 µm and a maximum of 5 µm (3.0%). Overall, the average difference (OCTOR versus Stratus) in retinal thickness for all subfields and the FCP was 2.3 µm. For the total macular volume, there was a mean difference of 0.06 mm3 (0.9%), a median of 0.06 mm3, and a maximum of 0.13 mm3 (2.0%).
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Correction of Retinal Boundaries in Cases with Detection Errors
Representative cases of diseases with frequent retinal boundary detection errors are illustrated in Figures 6 7 and 8 .
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Figure 6C illustrates the boundary lines as manually drawn by a human grader using the OCTOR software. In addition to drawing the correct location of the inner retinal surface, the grader also places lines indicating the location of the outer retinal surface and the inner RPE surface, thereby defining the subretinal space at the rim of the macular hole. After repeating this process for each of the six lines, the computer software computes the distances between the lines and performs a polar interpolation between the OCT scans to generate thickness maps. In addition to the corrected retinal height (the distance between the retinal surface and the inner surface of the RPE, which includes the retina and the subretinal space), separate maps are generated of the true retinal thickness, demonstrating the cuff of retinal thickening (Fig. 6D , asterisk) and the subretinal fluid cuff (Fig. 6E) .
Central Serous Chorioretinopathy
Figure 7A illustrates a common error in retinal boundary detection by the StratusOCT software in scans obtained from a patient with central serous chorioretinopathy (CSCR) with neurosensory retinal detachments. In this case, the outer retinal boundary does not correspond to the inner surface of the RPE. As a result, the retinal thickness map (Fig. 7B) does not depict retinal thickness, but rather retinal height from the RPE surface. In this case, one of the six radial line scans detected these boundaries inconsistently, resulting in a wedge-shaped artifact in the thickness map. Using the OCTOR software, the grader was able to distinguish and identify correctly the outer retinal and inner RPE surfaces (Fig. 7C) , thereby differentiating the retinal thickness (Fig. 7E) and subretinal space (Fig. 7F) from the retinal height (Fig. 7D) . In this case of CSCR, it is now apparent that the retina was not swollen, and the volume of subretinal fluid can be quantified.
Choroidal Neovascularization
Figure 8 illustrates the case of a patient with a fibrovascular pigment epithelial detachment (FVPED) associated with age-related macular degeneration. Despite the large PED, this patient had relatively good visual acuity (20/50). Figure 8A demonstrates a frequently observed artifact of the Stratus analysis. To generate a thickness map, the Stratus software "aligns" the A-scans, thereby effectively flattening areas of RPE elevation. Although the precise Stratus alignment algorithms are proprietary and not available, the alignment process appears to "slide" adjacent A-scans up or down, such that the hyperreflective signals in each A-scan corresponding to the RPE-choriocapillaris interface are positioned adjacent to each other in a relatively flat horizontal line. In addition, because it only measures the distance between the inner retinal surface and the inner RPE surface (the retinal height), the automated thickness map (Fig. 8B) does not convey any information regarding the height or volume of the PED. Furthermore, the retinal edema is not distinguishable from the subretinal fluid.
Although the alignment algorithm of the Stratus analysis can distort the retinal morphology, the clinician can always view the nonaligned raw data as shown in Figure 8C . However, using OCTOR software, a grader is able to identify the inner retinal surface, outer retinal surface, inner RPE surface, and the estimated "original" location of the RPE (by interpolation between the areas of flat RPE adjacent to the PED (Fig. 8D) . Thus, maps of the retinal thickness, subretinal fluid, and PED may also be generated (Figs. 8E 8F 8G 8H) .
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Discussion |
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In this study, the maximum difference in retinal thickness between the automated Stratus output and the manual OCTOR method in any subfield in any case was less than 8 µm (Table 4) . Among the 11 output parameters, the FCP thickness showed the largest differences both in comparisons between two attempts by one grader (mean percent difference of 1.6%), between two graders (mean percent difference of 2.4%), as well as in comparisons between manual grading and the automated Stratus output (mean percent difference of 2.4%). In contrast, for all other output parameters (in any type of comparison), the mean percentage difference was always less than 1.2%. The slightly greater discrepancy observed for the FCP is likely because the FCP is based on the averaging of only six points, whereas the other parameters (particularly the total macular volume) use many more A-scans.
This study also demonstrates that human graders can manually draw retinal boundaries using a computer mouse with good precision and reproducibility. Intra- and intergrader reproducibility appeared to be similar with this method. The small differences between gradings observed in this study are likely tolerable for most clinical or clinical research applications. Finally, although the interpolation algorithms used by the StratusOCT have not been published, the simple polar approximation used in this study appears to mimic the Stratus results closely.
Clinical OCT technology has dramatically evolved in just the last several years. The StratusOCT system can now render intraretinal and subretinal features in detail that would have seemed impossible only a few years ago. Future spectral domain36 37 38 39 and ultra-high-resolution40 OCT technology, with or without adaptive optics imaging, promises to improve imaging resolution even more, while decreasing acquisition times. With all these unique capabilities, OCT has quickly risen to the forefront of retinal diagnostic imaging. Despite limited research evidence to support its use in clinical decision-making, it is relied on as an important diagnostic tool by many ophthalmologists, and it is beginning to make its way into organized clinical trials. Many trials of macular edema, for example, require a minimum or maximum retinal thickness for eligibility, such as a FCP thickness greater than 300 µm. Errors in Stratus algorithms may affect eligibility decisions in these patients, and manual correction may be a valuable solution.
As OCT makes the transition from a research device to a critical clinical tool, care must be taken to ensure that its usage does not exceed its capabilities. For example, it would be quite easy to assume that the quantitative accuracy of retinal thickness measurements from this device should at least be equal to the superb imaging resolution evident in its cross-sectional images. Although most clinicians interpret the quantitative information in relation to the morphologic findings and their observations from the biomicroscopic examination, some clinicians may be tempted to rely on the machine’s automated, quantitative data summaries, particularly when morphologic changes over time are not striking.
Although this reliance on processed OCT information may be based on the assumption that the machine’s quantitative output is as accurate as its imaging output, mounting evidence29 41 suggests that this may not be the case. Based on recent advances in the understanding of the retinal anatomic correlates of the outer hyperreflective bands present on OCT,29 this study has identified a mean difference of 35 µm between the measured and true retinal thicknesses. Although this comprises only 15% of the measured value used by clinicians, it suggests that better software algorithms and anatomic knowledge will be needed before clinicians can fully rely on the quantitative output from these devices. Indeed, investigators such as Ishikawa et al.42 have worked to develop new automated segmentation algorithms that better detect the anatomically correct location of the RPE. In further support of the need for improved segmentation algorithms, several investigators have recently identified another set of errors in StratusOCT automated quantification that stem from problems with automated boundary detection.31 32 Although the effects of these errors on clinical management have not been extensively studied, they can only be expected to cause greater problems as clinicians increase their reliance on and confidence in OCT data.
Furthermore, clinically relevant intraretinal and subretinal features that are clearly evident to the human observer are not identified or quantified with current versions of automated segmentation software. For example, although retinal thickness is quantified in patients with macular edema, the volume of retinal cysts is not measured. In addition, an important limitation in eyes with subretinal fluid is that the fluid is often combined with the neurosensory retina by the Stratus software when calculating thickness measurements. For this reason, the traditional Stratus measurements are better termed "retinal height" (from the RPE) rather than retinal thickness.
Unfortunately, the inability of existing analyses to distinguish subretinal fluid volume from retinal volume results in a loss of potentially clinically useful data. In some patients with CNV, for example, a particular treatment may cause resorption of macular edema, but may have no effect on subretinal fluid or RPE elevation. Thus, this computer-assisted grading of OCT images may allow a grader to select and quantify the most clinically useful features, such as subretinal fluid, PED volume, or cystic spaces, in a given patient. Ongoing quality-assurance programs for masked reanalysis of OCT images at the reading center (DIRC) have demonstrated excellent reproducibility (Romano P, unpublished data, 2006) among graders for identifying the boundaries for these spaces. However, it is important to note that the reproducibility data (as well as the data described in this report) were obtained by certified reading center graders. Future studies quantifying various pathologic features, particularly those employing nonstandardized grading personnel, must demonstrate similar reproducibility of data before the results of those analyses can be considered meaningful.
Although manual correction of OCT boundary detection errors and the delineation of boundaries of other structures (such as subretinal fluid) described in this report are potentially useful, short-term solutions to the limitations of existing OCT software, it is important to recognize that ongoing advances in OCT hardware are likely to necessitate improvement in automated segmentation algorithms. New spectral (Fourier domain) OCT devices are capable of capturing more than 200 B-scans within a few seconds, but purely manual correction of boundary detection errors for this large number of scans is clearly not practical. It is hoped that recent breakthroughs in image processing and high-speed computing will allow the software advances to keep pace with the rapid development of enhanced imaging hardware.
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Footnotes |
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Disclosure: S.R. Sadda, None; S. Joeres, None; Z. Wu, None; P. Updike, None; P. Romano, None; A.T. Collins, None; A.C. Walsh, None
The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. 
1734 solely to indicate this fact.
Corresponding author: Srinivas R. Sadda, Doheny Eye Institute—DEI 3623, 1450 San Pablo Street, Los Angeles, CA 90033; sadda{at}usc.edu.
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