Deep learning-based detection of early biomarker in age-related macular degeneration in volume-merged high resolution optical coherence tomography

Type: MA thesis

Status: finished

Date: May 1, 2023 - November 2, 2023

Supervisors: Stefan Ploner, Jungeun Won (MIT), James Fujimoto (MIT), Andreas Maier

Eye diseases such as age-related macular degeneration (AMD) can cause significant visual impairment
and even vision loss, impacting patients’ quality of life. Early detection and diagnosis are crucial for
improving treatment outcomes. Optical coherence tomography (OCT) has become a vital medical
imaging technique for this purpose.

A promising biomarker for early AMD detection is the presence of basal laminar and basal linear
deposits in the outer retina1. However, conventional OCT devices often fail to detect these changes,
making them visible only in advanced stages or histological examinations.

In this thesis, an OCT prototype, which enables ultra-high-resolution imaging, is utilized to visualize
these deposits2. Moreover, motion correction and volume merging techniques were used to generate
consistent high-quality volumetric OCT data3. For accurate segmentation and thickness measurement
of basal laminar and basal linear deposits, deep learning algorithms will be investigated to provide a
data-driven automated approach with particular focus on the use of volumetric data and high-precision
results. For this purpose, a literature review of state-of-the-art approaches for retinal layer segmentation
will be conducted, followed by the development, training, and evaluation of a new problem-specific
designed deep learning approach and a comparison to existing baseline methods. A workflow utilizing
a high-performance computing cluster will be designed for efficient data-driven training.

The developed approach should be a step towards correlation analysis between the thickness and characteristics
of these deposits and the early stages of AMD solely using OCT images. A difficulty is the
particularly small size of this structure and its varying visibility, so that these characteristics must be
taken into account when working out the concept of the approach. The thesis aims to fully automate
the analysis of deposit thickness and visibility. Ultimately, such analysis could lead to better understanding
of the pathogenesis in AMD and in the long run improve treatment outcomes for patients.

1Sura, A. A., Chen, L., Messinger, J. D., Swain, T. A., McGwin, G., Freund, K. B., & Curcio, C. A. (2020). Measuring
the contributions of basal laminar deposit and Bruch’s membrane in age-related macular degeneration. Investigative
ophthalmology & visual science, 61(13), 19.
2Chen, S., Abu-Qamar, O., Kar, D., Messinger, J. D., Hwang, Y., Moult, E. M., … & Fujimoto, J. G. (2023).
Ultrahigh Resolution OCT Markers of Normal Aging and Early Age-related Macular Degeneration. Ophthalmology
Science, 3(3), 100277.
3Ploner, S., Chen, S., Won, J., Husvogt, L., Breininger, K., Schottenhamml, J., … & Maier, A. (2022, September). A
spatiotemporal model for precise and efficient fully-automatic 3D motion correction in OCT. In International Conference
on Medical Image Computing and Computer-Assisted Intervention (pp. 517-527). Cham: Springer Nature Switzerland.