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Related Experiment Video

Updated: May 10, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Automated segmentation tool for brain infusions.

Kathryn Hammond Rosenbluth1, Francisco Gimenez, Adrian P Kells

  • 1Department of Neurosurgery, University of California San Francisco-UCSF, San Francisco, California, United States of America. Kate.Rosenbluth@ucsf.edu

Plos One
|June 12, 2013
PubMed
Summary
This summary is machine-generated.

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This study introduces an automated tool for segmenting brain infusions seen in MRI scans. The computational method significantly reduces variability in measuring infusion volumes, improving efficiency for clinical trials.

Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Direct infusion of therapeutics into the brain is gaining traction for targeted drug delivery.
  • Real-time MRI with contrast agents aids infusion monitoring but manual segmentation is time-consuming and subjective.
  • Variability in manual segmentation can impact treatment efficacy and clinical trial outcomes.

Purpose of the Study:

  • To develop and validate a computational tool for automated segmentation of brain infusion distributions from MRI.
  • To reduce subjectivity and improve efficiency in analyzing infusion volumes.
  • To provide a reliable tool for neuro-oncology and gene therapy clinical trials.

Main Methods:

  • A Gaussian Mixture Model (GMM) was employed to classify pixels into infusate or background.

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  • The algorithm was implemented as a plug-in for the OsiriX® imaging platform.
  • Validation involved comparison with manual segmentations by four operators on 14 primate brain MRI datasets.

    • Main Results:

    • The automated tool accurately reproduced manual segmentation volumes.
    • A significant reduction in intra-operator variability by 67±18% was achieved.
    • The tool demonstrated efficiency and reliability in segmenting thalamic and putaminal infusions.

    Conclusions:

    • The developed computational tool offers an efficient and objective method for brain infusion segmentation using MRI.
    • This automated approach minimizes inter-observer variability, enhancing data reliability for clinical research.
    • The tool is poised to streamline and improve the precision of future neuro-oncology and gene therapy trials.