AIMC Topic: Tomography, Optical Coherence

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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.

Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks.

Journal of biophotonics
Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired b...

Automated Layer Segmentation of Retinal Optical Coherence Tomography Images Using a Deep Feature Enhanced Structured Random Forests Classifier.

IEEE journal of biomedical and health informatics
Optical coherence tomography (OCT) is a high-resolution and noninvasive imaging modality that has become one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is very crucial for doctors to diagnose and study retin...

Comparison of Machine-Learning Classification Models for Glaucoma Management.

Journal of healthcare engineering
This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with fou...