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Deep learning-driven MRI trigeminal nerve segmentation with SEVB-net.

Chuan Zhang1,2, Man Li3, Zheng Luo2

  • 1The First Affiliated Hospital, Jinan University, Guangzhou, China.

Frontiers in Neuroscience
|November 3, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning model, SEVB-Net, automatically segments the trigeminal nerve in MRI scans, improving diagnostic efficiency for trigeminal neuralgia (TN). This method is faster and more lightweight than existing approaches.

Keywords:
automatic segmentationdeep learningmagnetic resonance imagingtrigeminal nervetrigeminal neuralgia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Trigeminal neuralgia (TN) diagnosis and treatment are challenging due to severe pain.
  • Magnetic resonance imaging (MRI) is vital for TN diagnosis and understanding its pathology.
  • Manual segmentation of the trigeminal nerve in 3D MRI is time-consuming and subjective.

Purpose of the Study:

  • To introduce Squeeze and Excitation with BottleNeck V-Net (SEVB-Net), a novel deep learning model.
  • To achieve automatic segmentation of the trigeminal nerve in three-dimensional T2 MRI volumes.
  • To overcome the limitations of manual segmentation in terms of time and subjectivity.

Main Methods:

  • Developed SEVB-Net for end-to-end training using 3D T2 MRI images.
  • Evaluated SEVB-Net against V-Net and nnUNet using Dice similarity coefficient (DSC), sensitivity, and precision.
  • Compared segmentation time between manual and SEVB-Net with manual modification using the Mann-Whitney U test.

Main Results:

  • SEVB-Net achieved state-of-the-art performance, with DSC ranging from 0.6070 to 0.7923 when combined with ωDoubleLoss.
  • SEVB-Net demonstrated superior efficiency, significantly reducing parameters (2.20M), memory (11.41MB), and model size (17.02MB) compared to nnUNet.
  • Automatic segmentation with SEVB-Net significantly reduced segmentation time compared to manual methods (p < 0.001).

Conclusions:

  • SEVB-Net effectively automates the segmentation of the trigeminal nerve's root and main branches in 3D T2 MRI.
  • SEVB-Net offers comparable segmentation performance to nnUNet but with a significantly more lightweight architecture.
  • The proposed SEVB-Net model enhances computational efficiency and accuracy for trigeminal nerve segmentation in MRI.