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
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
Adi Szeskin, Roei Yehuda, Or Shmueli, Jaime Levy, Leo Joskowicz
Deep Learning and Computer Vision for Glaucoma Detection: A Review
Mona Ashtari-Majlan, Mohammad Mahdi Dehshibi, David Masip
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Xueshen Li, Zhenxing Dong, Hongshan Liu, Jennifer J. Kang-Mieler, Yuye Ling, Yu Gan
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
Md Abdul Kadir, Hasan Md Tusfiqur Alam, Daniel Sonntag
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
Linus Kreitner, Johannes C. Paetzold, Nikolaus Rauch, Chen Chen, Ahmed M. Hagag, Alaa E. Fayed, Sobha Sivaprasad, Sebastian Rausch, Julian Weichsel, Bjoern H. Menze, Matthias Harders, Benjamin Knier, Daniel Rueckert, Martin J. Menten
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation
Farnaz Sedighin, Andrzej Cichocki, Hossein Rabbani