@inbook{152f111bba8c416388c24ee123a12f4c,
title = "Intra-operative OCT (iOCT) Super Resolution: A Two-Stage Methodology Leveraging High Quality Pre-operative OCT Scans",
abstract = "Regenerative therapies have recently shown potential in restoring sight lost due to degenerative diseases. Their efficacy requires precise intra-retinal delivery, which can be achieved by robotic systems accompanied by high quality visualization of retinal layers. Intra-operative Optical Coherence Tomography (iOCT) captures cross-sectional retinal images in real-time but with image quality that is inadequate for intra-retinal therapy delivery. This paper proposes a two-stage super-resolution methodology that enhances the image quality of the low resolution (LR) iOCT images leveraging information from pre-operatively acquired high-resolution (HR) OCT (preOCT) images. First, we learn the degradation process from HR to LR domain through CycleGAN and use it to generate pseudo iOCT (LR) images from the HR preOCT ones. Then, we train a Pix2Pix model on the pairs of pseudo iOCT and preOCT to learn the super-resolution mapping. Quantitative analysis using both full-reference and no-reference image quality metrics demonstrates that our approach clearly outperforms the learning-based state-of-the art techniques with statistical significance. Achieving iOCT image quality comparable to preOCT quality can help this medical imaging modality be established in vitreoretinal surgery, without requiring expensive hardware-related system updates.",
keywords = "Image quality, iOCT, Super-resolution",
author = "Charalampos Komninos and Theodoros Pissas and Blanca Flores and Edward Bloch and Tom Vercauteren and S{\'e}bastien Ourselin and Cruz, {Lyndon Da} and Christos Bergeles",
note = "Funding Information: Supported by King{\textquoteright}s Centre for Doctoral Studies - Centre for Doctoral Training in Surgical & Interventional Engineering and funded in whole, or in part, by the Wellcome Trust [WT203148/Z/16/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 9th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 22-09-2022 Through 22-09-2022",
year = "2022",
month = sep,
day = "15",
doi = "10.1007/978-3-031-16525-2_11",
language = "English",
isbn = "9783031165245",
volume = "13576",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "105--114",
editor = "Bhavna Antony and Huazhu Fu and Lee, {Cecilia S.} and Tom MacGillivray and Yanwu Xu and Yalin Zheng",
booktitle = "International Workshop on Ophthalmic Medical Image Analysis",
}