Mr. Tom Murickan Profile

Tom Murickan

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Publications (1)

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.

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