AI Medical Compendium Topic:
Tomography, Optical Coherence

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Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features.

IEEE journal of biomedical and health informatics
Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical coherence tomography (OCT) has been recently introduced for intravascular imaging and assessment of cor...

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.

IEEE transactions on medical imaging
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o...

The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment.

Medical & biological engineering & computing
Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important ...

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula.

Journal of medical systems
Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the bes...

Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.

International ophthalmology
PURPOSE: In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM).

Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks.

IEEE transactions on medical imaging
Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In c...

Clinically applicable deep learning for diagnosis and referral in retinal disease.

Nature medicine
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common disease...

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm.