Dr. Nathan Hotaling Profile

Dr. Nathan Hotaling

at National Institutes of Health

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Author

Publications (2)

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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