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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Neural network training for cross-protocol radiomic feature standardization in computed tomography.

Vincent Andrearczyk1, Adrien Depeursinge1,2, Henning Müller1,3

  • 1University of Applied Sciences Western Switzerland (HES-SO), Institute of Information Systems, Sierre, Switzerland.

Journal of Medical Imaging (Bellingham, Wash.)
|June 18, 2019
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Radiomics feature stability is improved using novel neural network and adversarial training methods. These techniques reduce variations from scanner types, ensuring features reflect true patient tissue changes for better medical imaging analysis.

Keywords:
deep learningdomain adversarialquantitative imaging, radiomicsstandardization

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Area of Science:

  • Medical Imaging
  • Radiomics
  • Artificial Intelligence

Background:

  • Radiomics shows potential in medical studies but is limited by feature instability.
  • Image features vary significantly with scanner types, acquisition parameters, and reconstruction methods.
  • This instability hinders the accurate representation of physiopathological tissue changes.

Purpose of the Study:

  • To develop and compare methods for transforming quantitative image features.
  • To enhance feature stability across varying image acquisition parameters.
  • To preserve texture discrimination abilities while improving robustness.

Main Methods:

  • A two-layer neural network was used for nonlinear standardization of handcrafted and deep features.
  • Domain adversarial training was explored to achieve scanner invariance.
  • Experiments were conducted on a computed tomography texture phantom dataset with diverse imaging parameters.

Main Results:

  • The proposed methods successfully transformed quantitative image features.
  • Improved feature stability was achieved across different tomographic scanner types and acquisition parameters.
  • Texture discrimination abilities were preserved, demonstrating generalization to unseen data.

Conclusions:

  • The developed transformation methods enhance radiomics feature stability and robustness.
  • These techniques ensure that feature variations are representative of actual physiopathological changes.
  • The approach holds promise for more reliable and reproducible radiomics analysis in clinical practice.