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Related Concept Videos

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: Jul 17, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Identifying core MRI sequences for reliable automatic brain metastasis segmentation.

Josef A Buchner1, Jan C Peeken2, Lucas Etzel3

  • 1Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

A T1-weighted sequence with contrast enhancement (T1-CE) alone is sufficient for accurate brain metastasis segmentation. Combining T1-CE with T2 fluid-attenuated inversion recovery (T2-FLAIR) is crucial for effective edema segmentation.

Keywords:
Brain metastasesCNNDeep learningMRI sequencesSegmentationU-net

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Automated brain tumor segmentation often utilizes multiple MRI sequences.
  • Determining the optimal MRI sequences for brain metastasis (BM) segmentation is essential for improving automated approaches.

Purpose of the Study:

  • To compare different combinations of magnetic resonance imaging (MRI) sequences for effective automated brain metastasis (BM) segmentation.
  • To identify the minimal set of MRI sequences required for accurate BM and edema segmentation.

Main Methods:

  • Analysis of preoperative MRI data (T1-weighted ± contrast enhancement [T1-CE], T2-weighted [T2], T2 fluid-attenuated inversion recovery [T2-FLAIR]) from 339 patients with BMs.
  • Training and testing of a 3D U-Net model using various combinations of MRI sequences on independent cohorts.

Main Results:

  • A T1-CE-only model achieved the highest BM segmentation performance (median Dice Similarity Coefficient [DSC] of 0.96).
  • Models without T1-CE showed significantly lower performance (T1-only: DSC=0.70; T2-FLAIR-only: DSC=0.73).
  • The combination of T1-CE and T2-FLAIR yielded the best edema segmentation (DSC=0.93).

Conclusions:

  • A T1-CE-only protocol is adequate for brain metastasis segmentation.
  • The combination of T1-CE and T2-FLAIR is critical for edema segmentation.
  • Optimizing MRI sequences can streamline clinical workflows and enhance AI-driven segmentation accuracy.