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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Jun 18, 2025

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

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Modality redundancy for MRI-based glioblastoma segmentation.

Selene De Sutter1, Joris Wuts2,3, Wietse Geens4

  • 1Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), Brussels, Belgium. selene.de.sutter@vub.be.

International Journal of Computer Assisted Radiology and Surgery
|August 2, 2024
PubMed
Summary

Glioblastoma segmentation using fewer MRI modalities like T1CE-FLAIR can achieve accuracy comparable to using all four. Reducing input modalities for MRI segmentation is feasible and may improve clinical applicability.

Keywords:
BraTSDeep learningGlioblastomaMagnetic resonance imaging (MRI)SegmentationUncertainty

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Automated glioblastoma segmentation typically uses four MRI modalities: T1, contrast-enhanced T1 (T1CE), T2, and FLAIR.
  • Redundancy among these modalities may decrease model performance and increase risks associated with missing data in clinical settings.

Purpose of the Study:

  • To investigate the relevance and impact of different MRI modalities on glioblastoma segmentation accuracy.
  • To determine if reduced input sets can achieve comparable segmentation performance to the standard four-modality input.

Main Methods:

  • Trained multiple segmentation models using nnU-Net and SwinUNETR architectures with varying combinations of MRI input modalities.
  • Evaluated model performance based on segmentation accuracy and epistemic uncertainty.

Main Results:

  • Segmentation using T1CE or T1CE-FLAIR achieved accuracies comparable to the full four-modality input for specific tumor regions.
  • nnU-Net showed peak accuracy with T1CE-FLAIR-T1, indicating potential redundancy issues with more inputs.
  • SwinUNETR demonstrated statistically equivalent results between three-input and full-input models.

Conclusions:

  • A minimal-input model using T1CE-FLAIR is a viable alternative for glioblastoma segmentation.
  • Adding modalities beyond T1CE-FLAIR does not consistently improve accuracy and may degrade it, though it can reduce segmentation uncertainty.