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Related Concept Videos

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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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Related Experiment Video

Updated: May 5, 2026

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
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A Functional Shape Framework for the Detection of Multiple Sclerosis Using Optical Coherence Tomography Images.

Homa Tahvilian1, Raheleh Kafieh2, Fereshteh Ashtari3

  • 1Department of Electrical and Computer Engineering, Concordia University, MontrĂ©al, QC H3G 1M8, Canada.

Sensors (Basel, Switzerland)
|May 4, 2026
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Summary
This summary is machine-generated.

A novel F-shape framework using optical coherence tomography (OCT) retinal imaging effectively distinguishes multiple sclerosis (MS) patients from healthy individuals by analyzing ganglion cell-inner plexiform layer (GCIPL) thickness. This method significantly outperforms traditional approaches in MS detection.

Keywords:
atlas registrationfunctional shapemultiple sclerosisoptical coherence tomographysupport vector machine classifier

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Area of Science:

  • Ophthalmology
  • Neuroscience
  • Medical Imaging

Background:

  • Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease affecting the central nervous system.
  • Optical coherence tomography (OCT) provides non-invasive retinal imaging, with ganglion cell-inner plexiform layer (GCIPL) thickness serving as a key biomarker for MS progression.
  • Existing glaucoma detection methods using OCT and functional shape (F-shape) analysis demonstrate the potential of shape-based techniques in neuro-ophthalmology.

Purpose of the Study:

  • To develop and evaluate an F-shape-based framework for differentiating individuals with MS from healthy controls using GCIPL thickness measurements from OCT scans.
  • To compare the diagnostic performance of the proposed F-shape method against conventional sectoral-based analysis for MS detection.

Main Methods:

  • GCIPL thickness data from the macula region of OCT images were extracted for both MS patients and healthy subjects.
  • F-shape objects representing GCIPL thickness were registered to a common template using atlas registration.
  • Residual F-shapes were computed and used as input features for a support vector machine (SVM) classifier to detect MS.

Main Results:

  • The F-shape-based framework achieved superior accuracy, sensitivity, specificity, and AUC compared to sectoral-based schemes in distinguishing MS subjects.
  • Key factors contributing to the enhanced performance include a dense mesh on the region of interest, atlas registration for standardization, and the use of residual thicknesses as features.

Conclusions:

  • The F-shape-based approach represents a significant advancement in utilizing OCT imaging for MS detection.
  • This novel framework offers a more accurate and reliable method for monitoring MS by leveraging detailed GCIPL thickness variations.