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Related Concept Videos

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Updated: Apr 28, 2026

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Fully Automated Deep Learning-Based Pipeline for Evans Index Measurement from Raw 3D MRI.

Siavash Shirzadeh Barough1, Murat Bilgel2, Ameya Moghekar1

  • 1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21224, USA.

Neuroscience Informatics
|April 27, 2026
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Summary
This summary is machine-generated.

A new deep learning pipeline automates the Evans Index calculation from MRI scans, improving accuracy and reproducibility for diagnosing cerebrospinal fluid disorders like normal pressure hydrocephalus.

Keywords:
Deep LearningEvans IndexMagnetic Resonance ImagingNormal Pressure Hydrocephalus

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Ventriculomegaly is a key indicator in cerebrospinal fluid (CSF) disorders.
  • The Evans Index (EI) measures ventricular enlargement but manual calculation is variable.
  • Reproducibility challenges limit EI's use in large-scale studies.

Purpose of the Study:

  • To develop a fully automated deep learning pipeline for accurate Evans Index calculation.
  • To standardize EI measurement for improved reliability in clinical and research settings.
  • To overcome limitations of manual EI assessment in normal pressure hydrocephalus (NPH) diagnosis.

Main Methods:

  • A deep learning pipeline was created using T1-weighted MRI scans.
  • Automated landmark detection, AC-PC alignment, and ventricle/intracranial volume segmentation were employed.
  • Custom nnU-Net models were trained on 1,300 annotated scans for hydrocephalus detection.

Main Results:

  • Excellent internal validation with a Dice coefficient of 0.98.
  • Strong agreement with expert manual measurements in external validation (r=0.96, p<0.001).
  • No bias observed related to age, sex, or ventricular volume.

Conclusions:

  • The automated pipeline provides accurate and reproducible Evans Index assessment.
  • This method enhances scalability for clinical applications and research, especially in NPH.
  • Deep learning offers a robust solution for standardizing neuroimaging biomarkers.