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
Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography
Peng Liu, Jonas Golde, Joseph Morgenstern, Sebastian Bodenstedt, Chenpan Li, Yujia Hu, Zhaoyu Chen, Edmund Koch, Marcus Neudert, Stefanie Speidel
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
Debayan Bhattacharya, Sarah Latus, Finn Behrendt, Florin Thimm, Dennis Eggert, Christian Betz, Alexander Schlaefer
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
Arunava Chakravarty, Taha Emre, Oliver Leingang, Sophie Riedl, Julia Mai, Hendrik P. N. Scholl, Sobha Sivaprasad, Daniel Rueckert, Andrew Lotery, Ursula Schmidt-Erfurth, Hrvoje Bogunović
Deep-Learning-based Vasculature Extraction for Single-Scan Optical Coherence Tomography Angiography
Jinpeng Liao, Tianyu Zhang, Yilong Zhang, Chunhui Li, Zhihong Huang