@inbook{1e64d9d116784ab990dbd17f868400f9,
title = "Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach Using High Quality iOCT 3D Scans",
abstract = "Effective treatment of degenerative retinal diseases will require robot-assisted intraretinal therapy delivery supported by excellent retinal layer visualisation capabilities. Intra-operative Optical Coherence Tomography (iOCT) is an imaging modality which provides real-time, cross-sectional retinal images partially allowing visualisation of the layers where the sight restoring treatments should be delivered. Unfortunately, iOCT systems sacrifice image quality for high frame rates, making the identification of pertinent layers challenging. This paper proposes a Super-Resolution pipeline to enhance the quality of iOCT images leveraging information from iOCT 3D cube scans. We first explore whether 3D iOCT cube scans can indeed be used as high-resolution images by performing Image Quality Assessment. Then, we apply non-rigid image registration to generate partially aligned pairs, and we carry out data augmentation to increase the available training data. Finally, we use CycleGAN to transfer the quality between low-resolution (LR) and high-resolution (HR) domain. Quantitative analysis demonstrates that iOCT quality increases with statistical significance, but a qualitative study with expert clinicians is inconclusive with regards to their preferences.",
keywords = "Image quality assessment, iOCT, Super-resolution",
author = "Charalampos Komninos and Theodoros Pissas and Blanca Flores and Edward Bloch and Tom Vercauteren and S{\'e}bastien Ourselin and {Da Cruz}, Lyndon and Christos Bergeles",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 8th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87000-3_3",
language = "English",
isbn = "9783030869991",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "21--31",
editor = "Huazhu Fu and Garvin, {Mona K.} and Tom MacGillivray and Yanwu Xu and Yalin Zheng",
booktitle = "Ophthalmic Medical Image Analysis - 8th International Workshop, OMIA 2021, Held in Conjunction with MICCAI 2021, Proceedings",
address = "Germany",
}