optical coherence tomography
PulseAugur coverage of optical coherence tomography — every cluster mentioning optical coherence tomography across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New metric learning approach analyzes retinal images, outperforms neural networks
Researchers have developed a novel metric learning approach using normalized compression distance (NCD) combined with anisotropic structure-enhancing filters to analyze and visualize differences in 3D retinal images. Th…
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MARIO challenge shows AI matches doctors in AMD detection, lags in prediction
The MARIO challenge, hosted at MICCAI 2024, focused on using deep learning to analyze optical coherence tomography (OCT) images for the detection and monitoring of age-related macular degeneration (AMD). The challenge i…
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New spatial normalization framework improves OCT retinal layer segmentation
Researchers have developed a novel spatial normalization framework to improve the accuracy and generalization of retinal layer segmentation in Optical Coherence Tomography (OCT) images. This method, inspired by neuroima…
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New theory explores deep learning for speckle noise in imaging
Researchers have developed a minimax theory for likelihood-based deep learning to address speckle noise in imaging modalities like synthetic aperture radar and optical coherence tomography. This new framework handles bo…
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AI fuses OCT and OCTA images for improved diabetic retinopathy diagnosis
Researchers have developed a novel cross-modal fusion technique combining Optical Coherence Tomography (OCT) and OCT angiography (OCTA) en face images to improve the diagnosis of diabetic retinopathy. This method utiliz…
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AI framework precisely segments retinal biomarkers for AMD monitoring
Researchers have developed a new deep learning framework designed to precisely segment and measure retinal atrophy and ellipsoid zone thickness from optical coherence tomography (OCT) images. This automated system utili…
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New method enhances optical coherence tomography image quality for macular degeneration diagnosis
Researchers have developed a novel test-time adaptation method using flow matching to improve image quality in optical coherence tomography (OCT). This technique addresses inconsistencies in OCT images, particularly fro…
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AI Synthesizes Retinal Angiography Using Multi-Modal Imaging
Researchers have developed a new framework to synthesize fluorescein angiography (FFA) from fundus images and optical coherence tomography (OCT) scans. This method addresses limitations of previous approaches that relie…
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EyeMVP model enhances retinal analysis using paired CFP-OCT pretraining
Researchers have developed EyeMVP, a novel foundation model for retinal image analysis that integrates data from both color fundus photography (CFP) and optical coherence tomography (OCT). Pretrained on a large dataset …
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New Re-M3Dr framework improves multimodal regression for eye disease prediction
Researchers have developed a new multimodal regression framework called Re-M3Dr to improve the prediction of Mean Deviation (MD) in ophthalmology. While combining Optical Coherence Tomography (OCT) and fundus photograph…
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Survey reviews representation learning for retinal OCT image analysis
This paper surveys representation learning methods applied to Optical Coherence Tomography (OCT) images in ophthalmology. It reviews techniques from early deep learning to current foundation models and vision-language s…
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AI model detects retinal abnormalities without expert annotations
Researchers have developed a novel unsupervised anomaly detection framework for Optical Coherence Tomography (OCT) imaging, aiming to overcome the reliance on expert annotations for diagnosing retinal disorders. This ne…