As a non-invasive 3D optical imaging modality that operates on micrometer-scale, Optical Coherence Tomography (OCT) has become a standard of care in ophthalmology [1].
However, OCT imaging in general is a noisy process, with two of the typical noise sources being detection noise and laser speckle [2], [3]. There are multiple approaches for image enhancement. Due to the lack of ground truth data, deep learning approaches are often unsupervised. Noise2Noise [4] learns a denoising operation on images without actually needing clean versions of the samples during the training step. Instead, they use assumptions about the statistical nature of noise compared to actual data [4], [3]. An example where deep learning has been employed to improve OCT-related data before, is given in [3]. This work is primarily optimized for a low latency scenario and works by employing an unsupervised blind-spot denoising network that is trained on a masked version of the original data. A more complex approach to generate high quality data is volume fusion. Volume fusion is a 3 step process, which is comprised of motion correction of multiple OCT images, e.g., [6], illumination correction of brightness artifacts, e.g., [7], and merging of the resulting data. Results in [5] demonstrate signal enhancement and improve visibility of subtle retinal features on a micrometer scale. However, the authors of [5] suggest to use around 4–6 volumes for clean results. While using a lower number of images would be preferable for efficient clinical screening, using only two volumes could lead to gaps in the resulting image. Gaps result from eye motion during the OCT scanning process. Thus, it would be preferable to have an option to improve the results when using fewer scans, but still achieve levels of image quality similar to using more volumes.
The goal of this master’s thesis is to develop a method for denoising and inpainting of gaps in motioncorrected 3D-OCT images using supervised deep learning. We aim to improve the quality of images fused from fewer scans by training a denoiser with high quality scans that were combined and aggregated, using [6] and [7] as ground truth for our training.
The results will then be evaluated accordingly. Possible metrics for the evaluation of such a method could be structural similarity, peak signal-to-noise ratio or the contrast to noise ratio between the resulting image and the ground truth. Additionally, the correctness of inpainting will be evaluated by comparing the result to additional co-registered data that was not available to the image enhancement method.
In addition, this master’s thesis has the following requirements:
– literature research
– assembling of training and test sets with healthy data as well as data with different pathologies
– implementation of the method using a common deep learning framework
– submission of the method and the evaluation code
– Description of the performed work in a written thesis according to the lab’s thesis guidelines
– introductory and final presentation
References:
[1] Fujimoto J, Swanson E. “The Development, Commercialization, and Impact of Optical Coherence
Tomography.” In: Invest Ophthalmol Vis Sci. 2016 Jul 1;57(9):OCT1-OCT13, doi: 10.1167/iovs.16-19963.
PMID: 27409459; PMCID: PMC4968928.
[2] DuBose, Theodore B., et al.” Statistical models of signal and noise and fundamental limits of
segmentation accuracy in retinal optical coherence tomography.” In: IEEE transactions on medical
imaging, 2017, 37. Jg., Nr. 9, S. 1978-1988.
[3] Nienhaus, J., Matten, P., Britten, A. et al. “Live 4D-OCT denoising with self-supervised deep learning.”
In: Sci Rep 13, 5760 (2023), doi: 10.1038/s41598-023-32695-1
[4] Lehtinen, J. Noise2Noise: Learning Image Restoration without Clean Data. arXiv preprint
arXiv:1803.04189, 2018.
[5] Won, Jungeun, et al. “Topographic Measurement of the Subretinal Pigment Epithelium Space in
Normal Aging and Age-Related Macular Degeneration Using High-Resolution OCT.” In: Investigative
Ophthalmology & Visual Science, 2024, 65. Jg., Nr. 10, S. 18-18.
[6] Ploner, Stefan, et al. “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. Cham: Springer Nature Switzerland, 2022. S. 517-527, doi: 10.1007/978-3-031-16434-7_50
[7] Ploner, Stefan, et al. “A spatiotemporal illumination model for 3d image fusion in optical coherence
tomography.”, In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023. S. 1-
5., doi: 10.1109/ISBI53787.2023.10230526.