26 March 2023
In optical coherence tomography scans (OCTs) from multicenter studies, there is often large variability in image quality and resolution between scans. This impairs the consistency of biomarker quantification within studies, but also between different datasets. The aim of this study was to validate a super-resolution approach based on artificial intelligence (AI) for improving the resolution of OCTs (by increasing the density of the scan pattern) to consistently enhance data within studies to high-quality standards.
The MACUSTAR cohort, a European multicentre study, was used as a training set with 743 OCTs from 181 patients and validation set with 26 OCTs from 26 patients (n=3 no AMD, n=2 early AMD, n=18 intermediate AMD, n=3 late AMD). All scans were Heidelberg Spectralis OCTs with 241 B-scans. We trained a 3D diffusion model to generate high-resolution OCTs, which was used during evaluation to produce OCTs with 241 B-scans from OCTs with 120 B-scans using RePaint [1]. The performance was calculated using the mean squared error (MSE) on OCT volume-level between the generated B-scans and the original B-scans.
The MSE between the generated B-scans from the low-resolution OCTs and the original B-scans from the high-resolution OCTs was 0.006 ± 0.004 (mean ± SD). The figure below shows visual examples of the generated OCTs compared to the original B-scans in the validation set.
We showed the feasibility of the proposed approach to generate super-resolution OCTs, which is one of the required steps to standardize high-quality OCTs within multicenter studies. In extensions of this approach, coherence between the OCT and other modalities, such as en face imaging and other metadata, could be introduced, allowing the AI model to make better informed generative decisions.
The communication reflects the authors' views. Neither IMI nor the European Union, EFPIA, or any associated partners are responsible for any use that may be made of the information contained therein.
The header image of the so-called "OCT jungle" was generated by DALL·E 2.
Please use this when referring to the post or publication:
C. de Vente, A. Tufail, S. Schmitz-Valckenberg, M. Saßmannshausen, C. Hoyng and C.I. Sánchez on behalf of the MACUSTAR consortium. "OCT Super-Resolution for Data Standardization using AI: A MACUSTAR report", in: Association for Research in Vision and Ophthalmology, 2023.
C. de Vente, A. Tufail, S. Schmitz-Valckenberg, M. Saßmannshausen, C. Hoyng and C.I. Sánchez on behalf of the MACUSTAR consortium. "OCT Super-Resolution for Data Standardization using AI: A MACUSTAR report", in: Association for Research in Vision and Ophthalmology, 2023.
Poster (PDF)
@conference{Vent23c,
author = {de Vente, Coen and Tufail, A. and Schmitz-Valckenberg, S. and Saßmannshausen, M. and Hoyng, C. and S{\'a}nchez on behalf of the MACUSTAR consortium, Clara I},
title = {OCT Super-Resolution for Data Standardization using AI: A MACUSTAR report},
Methods: The MACUSTAR cohort, a European multicentre study, was used as a training set with 743 OCTs from 181 patients and validation set with 26 OCTs from 26 patients (n=3 no AMD, n=2 early AMD, n=18 intermediate AMD, n=3 late AMD). All scans were Heidelberg Spectralis OCTs with 241 B-scans. We trained a 3D diffusion model to generate high-resolution OCTs, which was used during evaluation to produce OCTs with 241 B-scans from OCTs with 120 B-scans. The performance was calculated using the mean squared error (MSE) on OCT volume-level between the generated B-scans and the original B-scans.
Results: The MSE between the generated B-scans from the low-resolution OCTs and the original B-scans from the high-resolution OCTs was 0.006 ± 0.004 (mean ± SD). Fig. 1 shows visual examples of the generated OCTs compared to the original B-scans in the validation set.
Conclusions: We showed the feasibility of the proposed approach to generate super-resolution OCTs, which is one of the required steps to standardize high-quality OCTs within multicenter studies. In extensions of this approach, coherence between the OCT and other modalities, such as en face imaging and other metadata, could be introduced, allowing the AI model to make better informed generative decisions.
},
year = {2023},
booktitle={Association for Research in Vision and Ophthalmology}
journal={Investigative Ophthalmology & Visual Science}
volume={64},
number={8},
pages={313--313},
publisher={The Association for Research in Vision and Ophthalmology},
url={https://iovs.arvojournals.org/article.aspx?articleid=2789903},
}
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