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Related Experiment Video

Updated: Jun 6, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Whole anterior visual pathway segmentation from high-resolution MRI using artificial intelligence.

Andrea Diociasi1,2, Emanuele Pravatà3,4, Luca Carmisciano5

  • 1Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova, Genova, Italy.

European Radiology Experimental
|June 5, 2026
PubMed
Summary

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This summary is machine-generated.

A new deep learning tool, aVP-seg, automates the segmentation of the anterior visual pathway (aVP) on MRI scans. This rapid, accurate method aids in assessing neuro-ophthalmic conditions like multiple sclerosis.

Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Ophthalmology

Background:

  • Manual segmentation of the anterior visual pathway (aVP) on high-resolution MRI is labor-intensive and subject to variability.
  • Accurate segmentation is crucial for quantitative analysis of the aVP in various neurological conditions.

Purpose of the Study:

  • To develop and validate a fully automated deep learning framework, "aVP-seg," for multiclass segmentation of the aVP.
  • To enable rapid and reproducible segmentation of optic nerves, chiasm, and optic tracts from CISS MRI.

Main Methods:

  • A cascaded two-stage 3D convolutional neural network was developed for automated segmentation.
  • The model was trained and validated on high-resolution CISS MRI data from 34 healthy controls and 46 MS patients.
Keywords:
Artificial intelligenceDeep learningMagnetic resonance imagingMultiple sclerosisVisual pathways

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  • Performance was evaluated using Dice similarity coefficient (DSC), Hausdorff distance (HD95), and volumetric similarity against manual segmentations by expert radiologists.

    • Main Results:

    • The aVP-seg achieved high overall agreement with ground truth (DSC 0.86 ± 0.03).
    • Strong boundary alignment (HD95 1.18 mm ± 0.54) and volumetric similarity (0.96 ± 0.04) were observed.
    • Accurate segmentation was consistent for optic nerves and chiasm, with slightly lower accuracy for optic tracts.

    Conclusions:

    • The automated aVP-seg framework provides accurate and rapid multiclass segmentation of the entire aVP from high-resolution CISS MRI.
    • This tool standardizes and accelerates the extraction of quantitative biomarkers for aVP integrity.
    • aVP-seg facilitates future assessments of optic pathway involvement in MS and other neuro-ophthalmic disorders.