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Updated: Sep 13, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI.

Nicole Tran1, Anisa Prasad1, Yan Zhuang1

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Summary
This summary is machine-generated.

MRSegmentator (MRSeg) outperformed TotalSegmentator MRI (TS) and TotalVibeSegmentator (VIBE) in segmenting multiple abdominal organs across various MRI sequence types. This study benchmarks these tools for improved multi-parametric MRI analysis in radiology.

Keywords:
AbdomenMRIMulti-ParametricSegmentationT1-weighted

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Multi-organ segmentation in multi-parametric MRI is crucial for correlating imaging biomarkers with disease status.
  • Publicly available tools like MRSegmentator (MRSeg), TotalSegmentator MRI (TS), and TotalVibeSegmentator (VIBE) exist for MRI segmentation.
  • Performance variations of these tools across different MRI sequences are not well-quantified.

Purpose of the Study:

  • To benchmark the performance of three public multi-organ segmentation tools (MRSeg, TS, VIBE) on specific MRI sequence types.
  • To evaluate the accuracy of these tools for abdominal structure segmentation in a curated dataset.

Main Methods:

  • A subset of 40 multi-parametric MRI volumes from the Duke Liver Dataset was curated, including 10 volumes each of pre-contrast fat saturated T1, arterial T1w, venous T1w, and delayed T1w phases.
  • Ten abdominal structures were manually annotated.
  • The performance of MRSeg, TS, and VIBE was evaluated using Dice score and Hausdorff Distance (HD) error.

Main Results:

  • MRSegmentator (MRSeg) achieved a Dice score of 80.7 ± 18.6 and a Hausdorff Distance (HD) error of 8.9 ± 10.4 mm.
  • MRSeg demonstrated significantly better performance (p < .05) across the evaluated MRI sequence types compared to TS and VIBE.

Conclusions:

  • MRSegmentator (MRSeg) is a superior tool for multi-organ segmentation in the evaluated multi-parametric MRI sequences.
  • This benchmarking provides critical insights for selecting appropriate segmentation tools in radiological applications.