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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency.

Stanislav Shimovolos1, Andrey Shushko1, Mikhail Belyaev2,3

  • 1Moscow Institute of Physics and Technology, 141701 Moscow, Russia.

Journal of Imaging
|September 22, 2022
PubMed
Summary
This summary is machine-generated.

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Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation.

Journal of imaging·2023
See all related articles

A new method, F-Consistency, improves deep learning for COVID-19 segmentation in CT scans. It addresses domain shift caused by different reconstruction kernels, enhancing model performance on unseen data.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning aids COVID-19 analysis in chest CT images.
  • Domain shift, particularly from reconstruction kernels, degrades model performance.
  • Robust algorithms are needed for clinical application with larger datasets.

Purpose of the Study:

  • To address the domain shift problem in COVID-19 CT image segmentation.
  • To compare existing domain adaptation techniques.
  • To propose and validate a novel unsupervised domain adaptation method.

Main Methods:

  • Investigated the impact of reconstruction kernel differences on COVID-19 segmentation.
  • Compared task-specific augmentation and unsupervised adversarial learning.
Keywords:
COVID-19 segmentationchest computed tomographyconvolutional neural networkdomain adaptation

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  • Proposed F-Consistency, an unsupervised method minimizing feature map MSE between paired CT images.

    • Main Results:

    • F-Consistency achieved a 0.64 Dice Score on unseen sharp kernels, outperforming the baseline (0.56).
    • F-Consistency improved paired image prediction Dice Score to 0.80 (baseline 0.46).
    • The method demonstrated better generalization on unseen kernels and without lesions.

    Conclusions:

    • F-Consistency effectively mitigates domain shift caused by varying CT reconstruction kernels.
    • The proposed method offers a robust solution for clinical deployment of deep learning in COVID-19 CT analysis.
    • Unsupervised adaptation using paired images with differing kernels is a promising direction.