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Related Concept Videos

Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Related Experiment Video

Updated: Aug 13, 2025

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
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Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.

Dawei Yang1, An Ran Ran1, Truong X Nguyen1

  • 1Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.

Diagnostics (Basel, Switzerland)
|January 21, 2023
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Summary

Optical coherence tomography angiography (OCT-A) combined with deep learning (DL) offers non-invasive retinal imaging for vascular diseases. Challenges remain for widespread clinical use, requiring standardized data and interpretation.

Keywords:
artificial intelligencedeep learningdiabetic macular ischemiadiabetic retinopathyglaucomaimage qualitymedical image analysisoptical coherence tomography angiographyretinal vascular diseases

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical coherence tomography angiography (OCT-A) enables non-invasive, depth-resolved visualization of retinal microvasculature.
  • OCT-A is valuable for investigating retinal vascular diseases and glaucoma by assessing microvascular changes.
  • Deep learning (DL) has shown promise in analyzing OCT-A images for tasks like quality control and segmentation.

Purpose of the Study:

  • To review current applications of DL in OCT-A image analysis.
  • To summarize challenges hindering the clinical deployment of DL in OCT-A.
  • To discuss future research directions for integrating DL with OCT-A.

Main Methods:

  • Review of existing literature on DL applications in OCT-A.
  • Analysis of DL performance in OCT-A image analysis tasks.
  • Identification of limitations for clinical translation.

Main Results:

  • DL has achieved good performance in OCT-A image quality control, segmentation, and classification.
  • DL enhances the potential for automated and efficient OCT-A analysis in eye clinics.
  • Clinical deployment is currently in the proof-of-concept stage.

Conclusions:

  • DL significantly enhances the clinical value of OCT-A for diagnosing and evaluating retinopathies.
  • Overcoming limitations such as small datasets and lack of standardization is crucial for real-world application.
  • Further research is needed to standardize DL in OCT-A for robust clinical implementation.