Optical Coherence Tomography
Optical coherence tomography (OCT) is a non-invasive imaging technique providing high-resolution cross-sectional images of tissues, primarily used in ophthalmology but with expanding applications in other fields. Current research focuses on improving image quality through deep learning-based denoising and reconstruction methods, often employing U-Net and Transformer architectures, as well as developing advanced segmentation algorithms for precise identification of anatomical structures and biomarkers. These advancements are significantly impacting disease diagnosis and prognosis, particularly in retinal diseases like age-related macular degeneration and glaucoma, by enabling automated analysis and quantitative assessment of disease progression.
Papers
ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma Grading
Zhiyuan Yang, Bo Zhang, Yufei Shi, Ningze Zhong, Johnathan Loh, Huihui Fang, Yanwu Xu, Si Yong Yeo
OCTolyzer: Fully automatic toolkit for segmentation and feature extracting in optical coherence tomography and scanning laser ophthalmoscopy data
Jamie Burke, Justin Engelmann, Samuel Gibbon, Charlene Hamid, Diana Moukaddem, Dan Pugh, Tariq Farrah, Niall Strang, Neeraj Dhaun, Tom MacGillivray, Stuart King, Ian J.C. MacCormick
Masked Image Modelling for retinal OCT understanding
Theodoros Pissas, Pablo Márquez-Neila, Sebastian Wolf, Martin Zinkernagel, Raphael Sznitman
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images
Jamie Burke, Justin Engelmann, Charlene Hamid, Diana Moukaddem, Dan Pugh, Neeraj Dhaun, Amos Storkey, Niall Strang, Stuart King, Tom MacGillivray, Miguel O. Bernabeu, Ian J. C. MacCormick