Some of the main causes of vision loss are eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy and glaucoma. Detecting these conditions early is critical and one of the main imaging modalities used in ophthalmology is optical coherence tomography (OCT). This thesis uses high resolution OCT images acquired at the New England Eye Center, Boston, MA. Existing motion correction and image fusion methods are used to generate high-quality volumetric OCT data (Ploner et al., 2024).
Building upon this data, this master thesis includes the development of boundary segmentation for multiple retinal layers, with specific focus on the anterior boundary of the ellipsoid zone. Additionally, the segmentation will be integrated in a pipeline for automated quantification of a biomarker.
The main tasks are:
● Evaluation of a promising new architecture for boundary segmentation, with particular consideration given to the Vision Transformer (Dosovitskiy et al., 2020)
● Development and evaluation of a method for automated quantification of an eye disease biomarker based on the segmented boundaries
Special attention will be given to the following aspects:
● Label efficiency, achieved either through task-specific pretraining or by utilizing a relevant foundational model, such as those proposed by Morano et al. (2025)
● Utilization of 3D data
The resulting model will be compared with the ground truth of the held-out test set. In addition, it will be evaluated against existing U-Net based boundary regression methods, such as those from He et al. (2019) and Karbole et al. (2024). The evaluation uses common regression metrics such as mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE).
The aim of this thesis is to contribute a model for the segmentation of retinal layer boundaries in OCT images, laying the groundwork for the automated quantification of a biomarker for AMD. This thesis shall provide a step towards earlier diagnosis, better monitoring of disease progression and improved clinical workflows.
References
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020, October 22). An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. arXiv.org.
He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2019). Fully convolutional boundary regression for retina OCT segmentation. Lecture Notes in Computer Science, 120–128.
Morano, J., Fazekas, B., Sükei, E., Fecso, R., Emre, T., Gumpinger, M., Faustmann, G., Oghbaie, M., Schmidt-Erfurth, U., & Bogunović, H. (2025, June 10). MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis. arXiv.org.
Karbole, W., Ploner, S. B., Won, J., Marmalidou, A., Takahashi, H., Waheed, N. K., Fujimoto, J. G., & Maier, A. (2024c). 3D deep learning-based boundary regression of an age-related retinal biomarker in high resolution OCT. In Informatik aktuell (pp. 350–355).
Ploner, S. B., Won, J., Takahashi, H., Karbole, W., Yaghy, A., Marmalidou, A., Schottenhamml, J., Waheed, N. K., Fujimoto, J. G., & Maier, A. (2024, May 5–9). A reliable, fully‑automatic pipeline for 3D motion correction and volume fusion enables investigation of smaller and lower‑contrast OCT features [Conference presentation]. Investigative Ophthalmology & Visual Science, 65(7), ARVO E‑Abstract 2794904.