AIMC Topic: Tomography, Optical Coherence

Clear Filters Showing 11 to 20 of 807 articles

Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.

Journal of biomedical optics
SIGNIFICANCE: Automated segmentation of periarterial and perivenous capillary-free zones (CFZs) in optical coherence tomography angiography (OCTA) can significantly improve early detection and monitoring of diabetic retinopathy (DR), a leading cause ...

Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs like mild eye redness, slightly blurred vision,...

Updates on novel and traditional OCT and OCTA biomarkers in nAMD.

Eye (London, England)
Predictivity of optical coherence tomography (OCT) examination for the development of neovascular age-related macular degeneration (nAMD) was demonstrated to be superior compared to other methods, suggesting it as an elective method for screening pur...

Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.

Scientific reports
A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence Tomography (OCT) images. This study, conducted at a university hospital, included 6335 participants ...

Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

BMJ open ophthalmology
PURPOSE: To develop an artificial intelligence (AI) system for detecting pathological patterns of diabetic macular oedema (DME) with fine-grained image categorisation using optical coherence tomography (OCT) images.

EFCNet enhances the efficiency of segmenting clinically significant small medical objects.

Scientific reports
Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitoring.However, existing models, including Convolutional Neural Networks (CNNs) and Transformers, strugg...

Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation.

Medical image analysis
Despite their effectiveness, current deep learning models face challenges with images coming from different domains with varying appearance and content. We introduce SegCLR, a versatile framework designed to segment images across different domains, e...

The potential of artificial intelligence reading label system on the training of ophthalmologists in retinal diseases, a multicenter bimodal multi-disease study.

BMC medical education
OBJECTIVE: To assess the potential of artificial intelligence reading label system on the training of ophthalmologists in a multicenter bimodal multi-disease study.

Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practice, providing non-invasive access to high-resolution retinal images. Segmentation of anatomical structures and pathological lesions in retinal OCT ima...