Optical Coherence Tomography

Latest papers 38

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LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning
Apr 7, 2026Yizhou Fang, Jian Zhong, Li Lin +1Semi-Supervised Medical Image Segmentation Medical Image Segmentation
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · Shenzhen Loop Area Institute, Shenzhen, China · Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
TAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis
Apr 6, 2026Xiaofei Su, Zengshuo Wang, Minghe Sun +2Vision Foundation Model Adaptation Medical Image Segmentation
Nankai University Optometry & Vision Science Institute, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Tianjin Key Laboratory of Intelligent Robotic, Institute of Robotics and Automatic Information System, Nankai University, Tianjin, 300350, China · Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, 518083, China
Retinal Layer Segmentation in OCT Images With 2.5D Cross-slice Feature Fusion Module for Glaucoma Assessment
Mar 25, 2026Hyunwoo Kim, Heesuk Kim, Wungrak Choi +1Medical Image Segmentation Optical Coherence Tomography
Department of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea · Institute of Vision Research, Department of Ophthalmology, Severance Hospital, Yonsei University, Seoul, 03722, South Korea
6D Robotic OCT Scanning of Curved Tissue Surfaces
Mar 23, 2026Suresh Guttikonda, Maximilian Neidhardt, Vidas Raudonis +1Hand-Eye Calibration Optical Coherence Tomography
Institute of Medical Technology and Intelligent Sys., Hamburg University of Technology, Germany · Interdisciplinary Competence Center for Interface Research, Hamburg, Germany · SustAInLivWork Center of Excellence, Kaunas, Lithuania +1
Vessel-Aware Deep Learning for OCTA-Based Detection of AMD
Mar 6, 2026Margalit G. Mitzner, Moinak Bhattacharya, Zhilin Zou +2Medical Image Analysis Explainable Medical Image Analysis
Stony Brook University, Stony Brook, NY, USA
Exploiting Intermediate Reconstructions in Optical Coherence Tomography for Test-Time Adaption of Medical Image Segmentation
Mar 5, 2026Thomas Pinetz, Veit Hucke, Hrvoje BogunovicMedical Image Segmentation Test-Time Adaptation
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria
Gradient based Severity Labeling for Biomarker Classification in OCT
Feb 23, 2026Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib +2Classification Pseudo-Labeling
OLIVES at the Center for Signal and Information Processing CSIP, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Retina Consultants Texas, Retina Consultants of America, Houston, Texas, USA
Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning
Feb 17, 2026Amal Lahchim, Lambros AthanasiouCoronary Artery Segmentation Medical Image Classification
¹²Electrical and Computer Engineering Department, University of Patras, Greece

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LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning

Yizhou Fang, Jian Zhong, Li Lin, Xiaoying Tang

Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · Shenzhen Loop Area Institute, Shenzhen, China · Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China

Semi-Supervised Medical Image Segmentation Medical Image Segmentation Optical Coherence Tomography Domain Generalization

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Optical Coherence Tomography (OCT) layer segmentation faces challenges due to annotation scarcity and heterogeneous label granularities across datasets. While semi-supervised learning helps alleviate label scarcity, existing methods typically assume a fixed granularity, failing to fully exploit cross-granularity supervision. This paper presents LUMOS, a semi-supervised universal OCT retinal layer segmentation framework based on a Dual-Decoder Network with a Hierarchical Prompting Strategy (DDN-HPS) and Reliable Progressive Multi-granularity Learning (RPML). DDN-HPS combines a dual-branch architecture with a multi-granularity prompting strategy to effectively suppress pseudo-label noise propagation. Meanwhile, RPML introduces region-level reliability weighing and a progressive training approach that guides the model from easier to more difficult tasks, ensuring the reliable selection of cross-granularity consistency targets, thereby achieving stable cross-granularity alignment. Experiments on six OCT datasets demonstrate that LUMOS largely outperforms existing methods and exhibits exceptional cross-domain and cross-granularity generalization capability.