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Virtual monochromatic image-based automatic segmentation strategy using deep learning method.

Lekang Chen1, Shutong Yu2, Yan Chen3

  • 1School of Physics, Beihang University, Beijing 102206, China.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|May 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MIAU-Net, a deep learning model using dual-energy CT virtual monochromatic images for accurate organ segmentation. It outperforms previous methods, identifying optimal energy levels for precise delineation.

Keywords:
Automatic segmentationDeep learningDual-energy CTVirtual monochromatic image

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Single-energy CT (SECT) image quality limits automatic segmentation accuracy.
  • Dual-energy CT (DECT) offers potential for improved segmentation, but its performance and optimal strategies require thorough investigation.

Purpose of the Study:

  • To propose and evaluate a novel deep learning model, MIAU-Net, for automatic segmentation of head organs-at-risk (OARs) using DECT-generated virtual monochromatic images (VMIs).
  • To compare MIAU-Net's performance against existing segmentation models and assess the impact of different VMI energy levels on segmentation accuracy.

Main Methods:

  • Retrospectively generated VMIs from 40 keV to 190 keV at 10 keV intervals from DECT scans of 46 patients.
  • Trained, validated, and tested MIAU-Net for automatic OAR segmentation using expert-delineated images.
  • Compared MIAU-Net with U-Net, Attention-UNet, nnU-Net, and TransFuse using Dice Similarity Coefficient (DSC); analyzed VMI energy impact on segmentation accuracy.

Main Results:

  • MIAU-Net achieved higher average DSCs for OARs compared to SECT-based methods, with specific values for brain stem (93.78%), optic chiasm (81.75%), lens (84.46%), mandible (92.85%), eyes (94.40%), and optic nerves (84.75%).
  • MIAU-Net demonstrated the highest average DSC (88.84%) and the lowest parameter count (14.54 M) among all tested models.
  • Optimal VMI energy levels identified: 60-80 keV for soft tissue and 100 keV for skeleton segmentation.

Conclusions:

  • A novel deep learning model (MIAU-Net) was proposed and validated for automatic segmentation using DECT-derived VMIs.
  • The study highlights the potential advantages of VMIs for automatic delineation and identifies OAR-specific optimal energy levels.