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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

Myungeun Lee1,2, Jong Hyo Kim3,4, Wookjin Choi5

  • 1Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of Korea.

Journal of Imaging Informatics in Medicine
|July 8, 2024
PubMed
Summary

TumorPrism3D software accurately segments brain tumors in MRI scans, outperforming 3DSlicer in speed and accuracy. This tool aids in quantitative analysis and AI-assisted brain tumor segmentation.

Keywords:
Brain TumorMagnetic Resonance ImagingSegmentationSemi-Automated

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Area of Science:

  • Medical imaging analysis
  • Computational neuroscience

Background:

  • Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.
  • Existing software may lack speed and user-friendliness for complex tumor segmentation.

Purpose of the Study:

  • To evaluate the performance of TumorPrism3D software for brain tumor segmentation.
  • To compare TumorPrism3D with 3DSlicer in terms of accuracy and speed.

Main Methods:

  • Brain magnetic resonance (MR) images from 185 glioblastoma multiforme patients were used.
  • TumorPrism3D was employed to segment contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components.
  • Segmentation accuracy was assessed using the Dice Similarity Coefficient (DSC) and compared to 3DSlicer.

Main Results:

  • TumorPrism3D achieved high accuracy with DSC values ranging from 0.83 to 0.91.
  • TumorPrism3D demonstrated superior accuracy compared to 3DSlicer (DSC 0.80–0.84).
  • TumorPrism3D was approximately 37.4% faster than 3DSlicer in the segmentation process.

Conclusions:

  • TumorPrism3D offers accurate and efficient semi-automated brain tumor segmentation.
  • The software's speed and accuracy support reproducible quantitative analysis and AI-assisted applications in neuroimaging.