3D Deep Learning-based Boundary Regression of an Age-related Retinal Biomarker in High Resolution OCT

Karbole W, Ploner S, Won J, Marmalidou A, Takahashi H, Waheed NK, Fujimoto JG, Maier A (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 350-355

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen DE

ISBN: 9783658440367

DOI: 10.1007/978-3-658-44037-4_90

Abstract

Vision is essential for quality of life, but is threatened by visionimpairing diseases like age-related macular degeneration (AMD). A recently proposed biomarker potentially to distinguish normal aging from AMD is the gap visualized between the retinal pigment epithelium (RPE) and the Bruch’s membrane. Due to lack of automated processing, to date, this gap was only described sparsely in histologic data or on optical coherence tomography (OCT) B-scans. By segmenting the posterior RPE boundary automatically for the first time, we enable fully-automatic quantification of the thickness of this gap in vivo across whole volumetric OCT images. Our novel processing pipeline leverages advancements in motion correction, volumetric image merging, and high resolution OCT. A novel 3D boundary regression network named depth map regression network (DMR-Net) estimates the gap thickness in the volume. As 3D networks require full-volume ground truth boundary labels, which are labor-intensive, we developed a novel semi-automatic labeling approach to refine existing labels based on the visibility of the gap with minimal user input. We demonstrate thickness maps across a wide age range of healthy participants (23 – 79 years). The median absolute error in the test set is 0.161 μm, which is well below the axial pixel spacing (0.89 μm). For the first time, our results allow spatially resolved analysis to investigate pathologic deviations in normal aging and AMD.

Authors with CRIS profile

Wenke Karbole Lehrstuhl für Informatik 5 (Mustererkennung) Stefan Ploner Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Massachusetts Institute of Technology (MIT) US United States (USA) (US) New England Eye Center US United States (USA) (US)

How to cite

APA:

Karbole, W., Ploner, S., Won, J., Marmalidou, A., Takahashi, H., Waheed, N.K.,... Maier, A. (2024). 3D Deep Learning-based Boundary Regression of an Age-related Retinal Biomarker in High Resolution OCT. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 350-355). Erlangen, DE: Wiesbaden: Springer Vieweg.

MLA:

Karbole, Wenke, et al. "3D Deep Learning-based Boundary Regression of an Age-related Retinal Biomarker in High Resolution OCT." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 350-355.

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