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Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study.

Xiaoliang Gong1, Chao Ma2, Panpan Yang2

  • 1College of Electronic and Information Engineering, Tongji University, Shanghai, PR China.

Acta Radiologica Open
|April 5, 2019
PubMed
Summary

Computer-aided pancreas segmentation using optimized 3D Dixon MRI is feasible. Water images from optimized Dixon MRI provide the best results for accurate pancreas segmentation.

Keywords:
DixonMagnetic resonance imagingpancreassegmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Anatomy

Background:

  • Accurate pancreas segmentation is crucial for pancreatic cancer radiotherapy, structural analysis, and functional evaluation.
  • Current segmentation methods face challenges in precision and efficiency.

Purpose of the Study:

  • To assess the feasibility of computer-aided pancreas segmentation using optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI).
  • To compare segmentation performance between routine and optimized Dixon MRI sequences.

Main Methods:

  • Seventeen healthy volunteers underwent routine and optimized 3D gradient echo (GRE) Dixon MRI at 3.0 T.
  • Computer-aided segmentation was performed using the Medical Imaging Interaction ToolKit (MITK) with threshold and morphological methods.
  • Performance was evaluated using Dice coefficients and 2D/3D visualizations.

Main Results:

  • The optimized Dixon MRI water images achieved the highest Dice coefficient (0.779 ± 0.048), significantly outperforming other sequences (P < 0.001).
  • Routine Dixon MRI water images yielded a Dice coefficient of 0.716 ± 0.033.
  • Opposed-phase images from both routine (0.633 ± 0.080) and optimized (0.415 ± 0.143) Dixon MRI showed lower segmentation accuracy.

Conclusions:

  • Computer-aided pancreas segmentation is feasible with Dixon MRI.
  • Optimized Dixon MRI water images offer superior accuracy and stability for pancreas segmentation.