AIMC Topic: Tomography, Optical Coherence

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DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Translational vision science & technology
PURPOSE: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.

Applications of deep learning in detection of glaucoma: A systematic review.

European journal of ophthalmology
Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical...

Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning.

IEEE journal of biomedical and health informatics
The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo. However, its applicati...

Machine Learning Techniques for Ophthalmic Data Processing: A Review.

IEEE journal of biomedical and health informatics
Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addre...

Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images.

IEEE journal of biomedical and health informatics
The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-a...

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging.

IEEE journal of biomedical and health informatics
Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment requirements are vastly heterogeneous and currently poorly plannable resulting in suboptimal treatment freq...

MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images.

IEEE journal of biomedical and health informatics
As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions...

UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.

IEEE journal of biomedical and health informatics
Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan images available. However, obtaining such fine-grained expert annotations is usually quite difficult an...

Dermal epidermal junction detection for full-field optical coherence tomography data of human skin by deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Full-field optical coherence tomography (FF-OCT) has been developed to obtain three-dimensional (3D) OCT data of human skin for early diagnosis of skin cancer. Detection of dermal epidermal junction (DEJ), where melanomas and basal cell carcinomas or...

Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.

The British journal of ophthalmology
AIM: To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).