Dr. Tiarnan D.L. Keenan Profile

Dr. Tiarnan D.L. Keenan

at National Eye Institute

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Author

Publications (7)

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 | 2 April 2024 Poster + Paper

Detection of reticular pseudodrusen on optical coherence tomography images

Amr Elsawy, Tiarnan Keenan, Elvira Agron, Qingyu Chen, Emily Chew, Zhiyong Lu

Proceedings Volume 12926, 1292632 (2024) https://doi.org/10.1117/12.3007014

KEYWORDS: Optical coherence tomography, Retina, Deep learning, Deep convolutional neural networks

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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 | 3 April 2023 Poster + Paper

Attention-based 3D convolutional networks for detection of geographic atrophy from optical coherence tomography scans

Amr Elsawy, Tiarnan D. Kenan, Qingyu Chen, Xiaoshuang Shi, Emily Chew, Zhiyong Lu

Proceedings Volume 12464, 124643B (2023) https://doi.org/10.1117/12.2654487

KEYWORDS: Optical coherence tomography, Network architectures, Eye, Visualization

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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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Showing 5 of 7 publications

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