Abstract

Optical Coherence Tomography (OCT) is an imaging modality that has been widely adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts. These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the acquired volumes as they substantially obfuscate the location of tissue interfaces. To deal with the artifacts and speckle noise patterns and accurately segment the shallowest tissue interface, we propose a cascaded neural network framework, which comprises of a conditional Generative Adversarial Network (cGAN) and a Tissue Interface Segmentation Network (TISN). The cGAN pre-segments OCT B-scans by removing undesired specular artifacts and speckle noise patterns just above the shallowest tissue interface, and the TISN combines the original OCT image with the pre-segmentation to segment the shallowest interface. We show the applicability of the cascaded framework to corneal datasets, demonstrate that it precisely segments the shallowest corneal interface, and also show its generalization capacity to limbal datasets. We also propose a hybrid framework, wherein the cGAN pre-segmentation is passed to a traditional image analysis-based segmentation algorithm, and describe the improved segmentation performance. To the best of our knowledge, this is the first approach to remove severe specular artifacts and speckle noise patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface. To the best of our knowledge, this is the first work to show the potential of incorporating a cGAN into larger deep learning frameworks for improved corneal and limbal OCT image segmentation. Our cGAN design directly improves the visualization of corneal and limbal OCT images from OCT scanners, and improves the performance of current OCT segmentation algorithms.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (1)

2018 (4)

2017 (9)

K. Bizheva, B. Tan, B. MacLellan, Z. Hosseinaee, E. Mason, D. Hileeto, and L. Sorbara, “In-vivo imaging of the palisades of vogt and the limbal crypts with sub-micrometer axial resolution optical coherence tomography,” Biomed. Opt. Express 8(9), 4141–4151 (2017).
[Crossref]

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L. Sisternes, G. Jonna, J. Moss, M. F. Marmor, T. Leng, and D. Rubin, “Automated intraretinal segmentation of sd-oct images in normal and age-related macular degeneration eyes,” Biomed. Opt. Express 8(3), 1926 (2017).
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L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
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X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks,” Neurocomputing 237, 332–341 (2017).
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A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
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2016 (4)

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for sd-oct images using region-based c-v model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
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2015 (3)

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2014 (2)

B. Wang, L. Kagemann, J. S. Schuman, H. Ishikawa, R. A. Bilonick, Y. Ling, I. A. Sigal, Z. Nadler, A. Francis, M. G. Sandrian, and G. Wollstein, “Gold nanorods as a contrast agent for doppler optical coherence tomography,” PLoS One 9(3), e90690 (2014).
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2013 (6)

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2012 (5)

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2011 (3)

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2009 (4)

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2008 (2)

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2005 (3)

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Supplementary Material (3)

NameDescription
» Visualization 1       Limbus - UHROCT scanner - 3x3mm Left image sequence - Original images from OCT sequences of the cornea and limbus. Middle image sequence - Corresponding pre-segmentation output from the Conditional Generative Adversarial Network (cGAN). Right ima
» Visualization 2       Cornea - Bioptigen SDOCT scanner - 6x6mm Left image sequence - Original images from OCT sequences of the cornea and limbus. Middle image sequence - Corresponding pre-segmentation output from the Conditional Generative Adversarial Network (cGAN).
» Visualization 3       Limbus - Leica SDOCT scanner - 4x4mm Left image sequence - Original images from OCT sequences of the cornea and limbus. Middle image sequence - Corresponding pre-segmentation output from the Conditional Generative Adversarial Network (cGAN). Righ

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Figures (20)

Fig. 1.
Fig. 1. Original B-scans from (a) a 6$\times$6mm corneal volume acquired by a custom SD-OCT scanner, (b) a 6$\times$6mm corneal volume and (c) a 3$\times$3mm corneal volume acquired by a UHR-OCT scanner, (d) a 4$\times$4mm limbal volume acquired by a hand-held Leica SD-OCT scanner, and (e)-(f) 4$\times$4mm limbal volumes acquired by a UHR-OCT scanner. Specular artifacts in (a)-(d) and poor visibility in (e)-(f) affect the precise delineation of the tissue interfaces.
Fig. 2.
Fig. 2. (a),(d) Original B-scans from a 4$\times$4mm limbal dataset acquired using a hand-held SD-OCT scanner and from a 3$\times$3mm corneal dataset acquired using a UHR-OCT scanner respectively. As proposed in previous algorithms [5,7,11,6471], vertical lines (magenta) denote the division of the image into three regions in order to deal with specular artifacts. (b),(e) Segmentation of the shallowest interface (cyan contour) by these algorithms failed due to presence of specular artifacts in different regions in the image. (c),(f) Segmentation result (red curve) from the proposed cascaded framework that accurately determined the location of shallowest tissue interface.
Fig. 3.
Fig. 3. Our proposed approach contains two frameworks: a cascaded framework (purple) and a hybrid framework (orange). First, a conditional Generative Adversarial Network (cGAN) takes an input OCT image, and produces an intermediate pre-segmentation image. In the pre-segmentation, pixels just prior to the shallowest tissue interface are set to 0 (black), while others are retained. In the cascaded framework, the pre-segmentation, along with the input image, are passed to a Tissue Interface Segmentation Network (TISN). The TISN predicts the location of shallowest interface by generating a binary segmentation mask (overlaid on the original image with a false color overlay; red - foreground, turquoise - background). In the hybrid framework, the pre-segmentation can be utilized by other segmentation algorithms. Ultimately, both frameworks fit a curve to the interface to produce the final segmentation.
Fig. 4.
Fig. 4. The CorNet architecture is the base used for training both the cGAN and TISN. The input to the cGAN is a two-channel image: the input OCT image and binary mask $w$ (see Sec. 3.1.2), and the output is a pre-segmented OCT image (orange box). The TISN gets a two-channel input (magenta and orange boxes), and the output is a binary mask (yellow box). The dark green blocks in the contracting path represent downsampling operations, while the blue blocks constitute upsampling computations. This model uses residual and dense connections to efficiently pre-segment the OCT image, and predict the location of the shallowest interface in the final output. The light blue module at the bottom of the model did not upsample feature maps, instead it functioned as a bottleneck to create outputs with the same size as those from the last layer.
Fig. 5.
Fig. 5. Each column shows the hypercolumn activation maps [80] of the UNET, BRUNET and CorNet architectures respectively for a corneal OCT B-scan. The activations were extracted from the downsampling layers 1 and 3, upsampling layers 1 and 3, and the layer with the highest receptive field (RF). Notice the improved structural detail in the CorNet activations as opposed to UNET and BRUNET.
Fig. 6.
Fig. 6. Comparing generated pre-segmentations between the UNET architecture used in the original cGAN implementation [31] against those generated by the CorNet architecture [29]. Original B-scans for two different limbal datasets are shown in (a). The corresponding generated pre-segmentations from the original UNET-based cGAN implementation (without weighted mask) is shown in (b), while those pre-segmentations for the modified UNET-based cGAN (with the weighted mask) is shown in (c). Similarly, the generated pre-segmentations from the CorNet-based cGAN (without weighted mask) is shown in (d), while those pre-segmentations for the modified CorNet-based cGAN (with the weighted mask) is shown in (e). Heat maps showing the difference between the original B-scan and the corresponding pre-segmentation by CorNet (with the weighted mask) is shown in (f). Note that the UNET did not remove the speckle patterns above the shallow tissue interface, while also encroaching upon the tissue boundaries without preserving them accurately.
Fig. 7.
Fig. 7. (a) Original B-scan of dimensions 1000 $\times$ 1024 pixels from a 6$\times$6mm corneal volume acquired by a custom SD-OCT scanner; (b) Four slices of dimensions 256 $\times$ 1024 pixels obtained by dividing the image in (a) width-wise; (c) Four corresponding predictions generated by the cGAN with the same dimensions as each input slice in (b); (d) The final pre-segmentation image recreated by putting together the slices in (c), with the final dimensions matching the 1000$\times$1024 pixels of the original B-scan. In the case of non-square OCT images, the magenta arrows show the extent of the overlapping regions in slices 3 and 4.
Fig. 8.
Fig. 8. (a) Expert annotation of an original B-scan in a 6$\times$6mm limbal volume acquired by Device 3, (b) Gold standard pre-segmentation image for training, (c) Binary mask $w$ used in Eq. (4) for training the cGAN, (d) Label map detailing the process of generating $w$ (see Sec. 3.1.2).
Fig. 9.
Fig. 9. (a) Original B-scan of dimensions 1000 $\times$ 1024 pixels from a 4$\times$4mm limbal volume acquired by Device 3; (b) Corresponding pre-segmentation obtained from our cGAN that is fed to the traditional and deep learning algorithms for final segmentation; (c) Comparing the segmentation result of a traditional algorithm executed without the pre-segmentation (magenta contour) against the result of the algorithm executed with the pre-segmentation (gold contour); (d) Comparing the segmentation result of a deep learning algorithm executed without the pre-segmentation (magenta contour) against the result of the algorithm executed with the pre-segmentation (gold contour). Note that the segmentation result closely follows the tissue interface boundary when the pre-segmentation is used.
Fig. 10.
Fig. 10. Corneal interface segmentation results for datasets acquired using Devices 1 and 2. Columns from left to right: (a) Original B-scans in corneal OCT datasets, (b) Pre-segmented OCT images from the cGAN with the specular artifact and speckle noise patterns removed just prior to the shallowest tissue interface, (c) Binary segmentation from the TISN overlaid in false color (red - foreground, turquoise - background) on the original B-scan, (d) Curve fit to the shallowest interface (red contour).
Fig. 11.
Fig. 11. Limbal interface segmentation results for datasets acquired using Devices 2 and 3. Columns from left to right: (a) Original B-scans in the limbal OCT datasets, (b) Pre-segmented OCT images from the cGAN with the specular artifact and speckle noise patterns removed above the shallowest tissue interface, (c) Binary segmentation from the TISN overlaid in false color (red - foreground, turquoise - background) on the original B-scan, (d) Curve fit to the shallowest interface (red contour).
Fig. 12.
Fig. 12. (a)-(c) HD error and (d)-(f) MADLBP error comparison for the corneal datasets acquired with Devices 1 and 2 respectively. In the boxplots, the segmentation results obtained for each baseline method are contrasted against expert grader (blue) and trained grader (red) annotations, while the Inter-Grader (IG) variability is shown in yellow.
Fig. 13.
Fig. 13. Quantitative estimation of the benefit of pre-segmenting the corneal OCT image. All the baselines were grouped into two categories: Traditional Comparison (TC; TWOPS vs TWPS), and Deep Learning Comparison (DLC; DLWOPS vs DLWPS). The first column corresponds to the former, and the second column corresponds to the latter. For each corneal test dataset, the image with the maximum HD error was found over all images in the sequence, and the image location in the sequence was stored. This was done only for the TWOPS and DLWOPS baselines respectively. The stored location indicies were then used to retrieve the corresponding HD errors from the TWPS and DLWPS baselines respectively. This procedure was repeated for each grader and plotted. G1 : without pre-segmentation (purple curve), with pre-segmentation (black curve). G2 : without pre-segmentation (yellow curve), with pre-segmentation (gray curve).
Fig. 14.
Fig. 14. Mean (circle) and Standard Deviation (error bars) of the Hausdorff Distance error for corneal datasets acquired with Devices 1 and 2 respectively. All the baselines were grouped into two categories: Traditional Comparison (TC; TWOPS vs TWPS), and Deep Learning Comparison (DLC; DLWOPS vs DLWPS). The mean and standard deviation error bars are shown only for the expert grader (G1) annotation. The With Out Pre-Segmentation (WOPS) results are shown by magenta error bars, while the With Pre-Segmentation (WPS) results are shown in black.
Fig. 15.
Fig. 15. (a)-(b) HD error and (c)-(d) MADLBP error comparison for the limbal datasets acquired with Devices 2 and 3 respectively. For the limbal datasets, the segmentation results obtained for each baseline method were contrasted exclusively against the expert annotations (G1). This graph plots the errors across all limbal datasets, including the failure cases. In contrast to Fig. 19, note the increased segmentation error in the DLWPS baseline due to imprecise pre-segmentations.
Fig. 16.
Fig. 16. Quantitative estimation of the benefit of pre-segmenting the limbal OCT image. All the baselines were grouped into two categories: TC (TWOPS vs TWPS), and DLC (DLWOPS vs DLWPS). The first column corresponds to the former, and the second column corresponds to the latter. For each test dataset, the image with the maximum HD error was found over all images in the sequence, and the image location in the sequence was stored. This was done only for the TWOPS and DLWOPS baselines respectively. The stored location indicies were then used to retrieve the corresponding HD errors from the TWPS and DLWPS baselines respectively. This procedure was done for only the expert grader and plotted. G1 : without pre-segmentation (purple curve), with pre-segmentation (black curve). Errors shown after red vertical line correspond to the failure cases of our approach.
Fig. 17.
Fig. 17. Mean (circle) and Standard Deviation (error bars) of the Hausdorff Distance error for limbal datasets acquired with Devices 2 and 3 respectively. All the baselines were grouped into two categories: Traditional Comparison (TC; TWOPS vs TWPS), and Deep Learning Comparison (DLC; DLWOPS vs DLWPS). The mean and standard deviation error bars are shown only for the expert grader (G1) annotation. The With Out Pre-Segmentation (WOPS) results are shown by magenta error bars, while the With Pre-Segmentation (WPS) results are shown in black. Errors shown after red vertical line correspond to the failure cases of our approach.
Fig. 18.
Fig. 18. Failure cases of our cascaded framework on three challenging limbal OCT datasets. Columns from left to right: (a) Original B-scans in the limbal OCT volumes, (b) cGAN pre-segmentation results that imprecisely removed speckle noise patterns and specular artifacts above the shallowest tissue interface, (c) The binary segmentation masks from the TISN overlaid in false color (red - foreground, turquoise - background) on the original B-scans, (d) Curve fit to the shallowest interface (red contour).
Fig. 19.
Fig. 19. (a)-(b) HD error and (c)-(d) MADLBP error comparison for the limbal datasets acquired with Devices 2 and 3 respectively. For the limbal datasets, the segmentation results obtained for each baseline method were contrasted exclusively against the expert annotations (G1). These graphs plot errors for the successful segmentation results on 15 limbal test datasets.
Fig. 20.
Fig. 20. Segmenting the shallowest tissue interface in OCT datasets, wherein the OCT scanner commenced imaging from the limbus and crossed over into the cornea, thereby encompassing the limbal junction. (a),(b) B-scans #1 and #300 in an OCT dataset corresponding to the limbus and the cornea respectively. (c),(d) B-scans #1 and #220 in a different OCT dataset corresponding to the limbus and the cornea respectively. (e),(f),(g),(h) Segmentation (red curve) of the shallowest tissue interface in images shown in (a),(b),(c) and (d) respectively. Note the partial overlap of the limbal (left) and corneal (right) region in the B-scan in (d), and the correct identification of the shallowest interface in (h).

Tables (2)

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Table 1. Statistical significance between our cascaded framework (DLWPS) against each baseline method for all the corneal datasets from Devices 1 and 2.

Tables Icon

Table 2. Statistical significance between our cascaded framework (DLWPS) against each baseline method for 15 (out of 18) limbal datasets acquired from Devices 2 and 3.

Equations (6)

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G = a r g   min G   max D   L c G A N ( G , D ) + λ L 1 ( G )
L c G A N ( G , D ) = E x , y t [ l o g D ( x , y t ) ] + E x , z [ l o g ( 1 D ( x , G ( x , z ) ) ]
L 1 = E x , y t , z [ y t G ( x , z ) 1 ]
L w 1 = E x , y , z [ α w y t G ( x , z ) 1 + ( 1 w ) y t G ( x , z ) 1 ]
MADLBP = 1 X x = 0 X 1 | y G ( x ) y S ( x ) |
HD = max ( max p G   d S ( p ) ,   max p S   d G ( p ) )

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