Dr. Catherine A. Cukras Profile

Dr. Catherine A. Cukras

at National Eye Institute

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Publications (10)

Proceedings Article | 3 April 2024 Presentation + Paper

Fovea segmentation in fundus autofluorescence images using ground truth annotations from three-dimensional optical coherence tomography images

Souvick Mukherjee, Cameron Duic, Tom Murickan, Tharindu De Silva, Tiarnan Keenan, Emily Chew, Catherine Cukras

Proceedings Volume 12927, 129271A (2024) https://doi.org/10.1117/12.3006409

KEYWORDS: Image segmentation, Optical coherence tomography, Autofluorescence, Retina, Retinal diseases, Infrared imaging, 3D image processing, Visualization, Pathology, Fovea centralis

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Purpose: This work aims to automatically identify the fovea on 2-dimensional fundus-autofluorescence (FAFs) images in patients with age-related-macular-degeneration (AMD) using the definitions from 3-dimensional spectral-domain-optical-coherence-tomography (SD-OCT) imaging. Segmenting the fovea, a highly specialized area of the retina, in vicinity of hypo-autofluorescence in FAF images will aid in objective evaluation of AMD based structural disease features with respect to distance from fovea. Methods: A semi-automated software was used to create fovea-annotations in volumetric SD-OCT images. Acquired FAF images for the same SD-OCT visits were registered to the enface SD-OCT projections, to create a pixel-to-pixel overlap between registered FAFs and SD-OCTs. A U-Net based segmentation network, trained using OCT-registered-FAFs and corresponding foveal-annotations from SD-OCTs, was used to automatically segment foveas from the registered 2D FAF images. Results: The dataset consisted of multimodal-images from AMD patients with 900 (80%) images used for training and 222 (20%) images used in the test-set. The mean euclidean-distance-error for the test-set w.r.t the OCT-determined-ground-truth was found to be 103.5±81.4 µm, and which improved to 83.4±57.9 µm with data-augmentation-based-training. Fovea-identification in FAF images with advanced-AMD disease consisting of geographic-atrophy (GA) test subset were compared between 3 sources and the OCTdetermined-ground-truth: (1) the U-Net algorithm (using the GA test subset (111.7±46.7 μm)), (2) readers at the Wisconsin-reading-center (165±77.5 μm) and a (3) retina-physician (169.9±109.4 μm). Conclusion: Our work demonstrates the potential of using 2D FAF images to predict foveal-locations, especially in visuallychallenging-scenarios where hypo-autofluorescent fovea is surrounded with advanced-disease that alters the normal autofluorescence patterns. The results demonstrate that the developed algorithm has clinically useful performance in segmenting the fovea in FAF images which will enable critical correlation with visual-acuity and the basis for referencing the standardized measures of features relative to the fovea – such as monitoring and tracking the growth of GA and other retina-disease related changes.

Proceedings Article | 7 April 2023 Presentation + Paper

Drusen segmentation in color fundus photographs for drusenoid pigment epithelial detachment patients based on ground-truth derived from SD-OCTs

Souvick Mukherjee, Cameron Duic, Tharindu De Silva, Tiarnan Keenan, Alisa Thavikulwat, Emily Chew, Catherine Cukras

Proceedings Volume 12465, 1246515 (2023) https://doi.org/10.1117/12.2653995

KEYWORDS: Image segmentation, Optical coherence tomography, Image processing algorithms and systems, Eye, Algorithm development, Retinal diseases

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Proceedings Article | 17 March 2023 Presentation

Structural metrics of the photoreceptor–pigment epithelium–choriocapillaris complex measured with 3.4 MHz adaptive optics–optical coherence tomography

Daniel Hammer, Oliver Wolcott, Achyut Raghavendra, Zhuolin Liu, Samira Aghayee, Osamah Saeedi, Catherine Cukras

Proceedings Volume PC12360, PC123600G (2023) https://doi.org/10.1117/12.2650566

KEYWORDS: Adaptive optics, Tomography, Macula, Rods, Retina, Optical coherence tomography, Image segmentation, Cones, Capillaries, Adaptive optics optical coherence tomography

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Proceedings Article | 4 April 2022 Presentation + Paper

Semi-supervised learning approach for automatic detection of hyperreflective foci in SD-OCT imaging

Tharindu De Silva, Kristina Hess, Cameron Duic, Souvick Mukherjee, Hector Sandoval, Jessica Aduwo, Tiarnan Keenan, Emily Chew, Catherine Cukras

Proceedings Volume 12033, 120333I (2022) https://doi.org/10.1117/12.2613192

KEYWORDS: Data modeling, Performance modeling, Inspection, Optical coherence tomography, Detection and tracking algorithms, Reflectivity, Process modeling, Eye, Coherence imaging, Yield improvement

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Proceedings Article | 4 April 2022 Presentation + Paper

Device specific SD-OCT retinal layer segmentation using cycle-generative adversarial networks in patients with AMD

Souvick Mukherjee, Tharindu De Silva, Gopal Jayakar, Peyton Grisso, Henry Wiley, Tiarnan Keenan, Alisa Thavikulwat, Emily Chew, Catherine Cukras

Proceedings Volume 12033, 120333H (2022) https://doi.org/10.1117/12.2613066

KEYWORDS: Image segmentation, 3D modeling, Retina, Image processing algorithms and systems, Detection and tracking algorithms, Signal to noise ratio, 3D image processing, Image contrast enhancement, Medical image reconstruction, Medical image processing

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Proceedings Article | 4 April 2022 Presentation + Paper

Retinal layer segmentation for age-related macular degeneration patients with 3D-UNet

Souvick Mukherjee, Tharindu De Silva, Gopal Jayakar, Peyton Grisso, Henry Wiley, Tiarnan Keenan, Alisa Thavikulwat, Emily Chew, Catherine Cukras

Proceedings Volume 12033, 120333J (2022) https://doi.org/10.1117/12.2612991

KEYWORDS: 3D modeling, Retina, Image segmentation, Optical coherence tomography, Eye models, Data modeling, Feature extraction, Eye, Statistical modeling

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Proceedings Article | 4 April 2022 Poster + Paper

Hybrid transformer for lesion segmentation on adaptive optics retinal images

Jianfei Liu, Joanne Li, Amday Wolde, Catherine Cukras, Johnny Tam

Proceedings Volume 12033, 120331W (2022) https://doi.org/10.1117/12.2612379

KEYWORDS: Image segmentation, Transformers, Adaptive optics, Computer programming, Convolution, Retinal scanning, Medical imaging, Convolutional neural networks, Tissues, Eye

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Proceedings Article | 5 March 2021 Presentation

Towards in vivo imaging of ganglion cell layer somas using widely-available clinical OCT devices

Furu Zhang, Catherine Cukras, Daniel Hammer, Zhuolin Liu

Proceedings Volume 11623, 116230Y (2021) https://doi.org/10.1117/12.2582785

KEYWORDS: Optical coherence tomography, In vivo imaging, Adaptive optics, Adaptive optics optical coherence tomography, Retinal scanning, Image quality, Human vision and color perception, Clinical trials

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Proceedings Article | 15 February 2021 Presentation

Automatic detection of ellipsoid zone loss due to Hydroxychloroquine retinal toxicity from SD-OCT imaging

Tharindu De Silva, Gopal Jayakar, Peyton Grisso, Emily Chew, Nathan Hotaling, Catherine Cukras

Proceedings Volume 11597, 115970Q (2021) https://doi.org/10.1117/12.2582153

KEYWORDS: Toxicity, Optical coherence tomography, Image segmentation, Coherence imaging, Retina, Reflectivity, Performance modeling, Network architectures, Image quality, Image processing algorithms and systems

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Proceedings Article | 10 March 2020 Paper

Feature-based retinal image registration for longitudinal analysis of patients with age-related macular degeneration

Tharindu De Silva, Nathan Hotaling, Emily Chew, Catherine Cukras

Proceedings Volume 11313, 113132Z (2020) https://doi.org/10.1117/12.2549969

KEYWORDS: Image registration, Image restoration, Image analysis, Analytical research, Feature extraction, Infrared imaging, Algorithm development, Image segmentation, Image quality, Eye

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Spatial alignment of longitudinally acquired retinal images is necessary for the development of image-based metrics identifying structural features associated with disease progression in diseases such as age-related macular degeneration (AMD). This work develops and evaluates a feature-based registration framework for accurate and robust registration of retinal images. Methods: Two feature-based registration approaches were investigated for the alignment of fundus auto-fluorescence images. The first method used conventional SIFT local feature descriptors to solve for the geometric transformation between two corresponding point-sets. The second method used a deep-learning approach with a network architecture mirroring the feature localization and matching process of the conventional method. The methods were validated using clinical images acquired in an ongoing longitudinal study of AMD and consisted of 75 patients (145 eyes) with 4 year follow up imaging. In the deep-learning method, 113 image pairs were used during training (with the ground truth provided from manually verified SIFT feature registration) and 20 image pairs were used for testing (with the ground truth provided from manual landmark annotation). Results: Conventional method using SIFT features demonstrated target registration error (mean ± std) = 0.05 ± 0.04 mm, substantially improving the alignment from the initialization with error = 0.34 ± 0.22 mm. The deep-learning method, on the other hand, exhibited error = 0.10 ± 0.07 mm. While both methods improved upon the initial misalignment, SIFT method showed the best overall geometric accuracy. However, deep-learning method exhibited robust performance (error = 0.15 ± 0.09 mm) in the 7% of cases that SIFT method exhibited failures (error = 3.71 ± 6.36 mm). Conclusion: While both methods demonstrated successful performance, SIFT method exhibited the best overall geometric accuracy whereas deep-learning method was superior in terms of robustness. Achieving accurate and robust registration is essential in large-scale studies investigating factors underlying retinal disease progression such as in AMD.

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