AIMC Topic: Tomography, Optical Coherence

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Self-supervised iterative refinement learning for macular OCT volumetric data classification.

Computers in biology and medicine
We present self-supervised iterative refinement learning (SIRL) as a pipeline to improve a type of macular optical coherence tomography (OCT) volumetric image classification algorithms. In this type of algorithms, first, two-dimensional (2D) image cl...

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance ...

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.

IEEE transactions on medical imaging
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a priori definitions of these regions...

Spatio-temporal deep learning models for tip force estimation during needle insertion.

International journal of computer assisted radiology and surgery
PURPOSE: Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. He...

Automated OCT angiography image quality assessment using a deep learning algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).

Automated summarisation of SDOCT volumes using deep learning: Transfer learning vs de novo trained networks.

PloS one
Spectral-domain optical coherence tomography (SDOCT) is a non-invasive imaging modality that generates high-resolution volumetric images. This modality finds widespread usage in ophthalmology for the diagnosis and management of various ocular conditi...

Automated segmentation of macular edema in OCT using deep neural networks.

Medical image analysis
Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema becau...

Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.

PloS one
OBJECTIVE: To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the ...

Artificial Intelligence and Optical Coherence Tomography Imaging.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
This review article aimed to highlight the application and use of artificial intelligence (AI) in optical coherence tomography (OCT) imaging in ophthalmology. Artificial intelligence programs seek to simulate intelligent human behavior in computers. ...