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Related Experiment Video

Updated: Jun 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Measuring brain lesion progression with a supervised tissue classification system.

Evangelia I Zacharaki1, Stathis Kanterakis, R Nick Bryan

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. Eva.Zacharaki@uphs.upenn.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 5, 2008
PubMed
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This study introduces a computer-assisted method for segmenting white matter lesions (WMLs) using MRI scans. The novel approach enhances accuracy in tracking WML progression for better disease monitoring.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • White Matter Lesions (WMLs) are linked to cardiovascular disease, vascular conditions, and normal aging processes.
  • Accurate quantitative analysis of WMLs is crucial for large-scale clinical trials and disease monitoring.
  • Existing methods may face challenges with spatial and temporal variance in longitudinal studies.

Purpose of the Study:

  • To develop and validate a computer-assisted segmentation method for accurate WML quantification.
  • To improve the longitudinal stability of WML measurements using advanced preprocessing techniques.
  • To enhance the reliability of WML progression estimation in multi-site clinical studies.

Main Methods:

  • Utilized multi-parametric Magnetic Resonance Imaging (MRI) sequences for feature extraction.

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Related Experiment Videos

Last Updated: Jun 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

  • Implemented a joint histogram equalization framework to reduce data variance and improve temporal stability.
  • Employed a Support Vector Machine (SVM) classifier with AdaBoost for robust WML segmentation.
  • Validated the method on 23 patients across 3 imaging sites with longitudinal data.

    • Main Results:

    • The computer-assisted system demonstrated consistent WML segmentation in 4D data.
    • The method showed robustness and accuracy when compared to manual segmentation by an expert neuroradiologist.
    • Preprocessing techniques effectively reduced spatial and temporal variance, enhancing longitudinal stability.

    Conclusions:

    • The proposed computer-assisted segmentation method offers a reliable tool for WML analysis.
    • This technique facilitates accurate disease monitoring by improving the estimation of lesion progression.
    • The approach holds promise for application in large clinical trials involving WML assessment.