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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Registration of Pathological Images.

Xiao Yang1, Xu Han1, Eunbyung Park1

  • 1UNC Chapel Hill, Chapel Hill, USA.

Simulation and Synthesis in Medical Imaging : ... International Workshop, SASHIMI ..., Held in Conjunction with MICCAI ..., Proceedings. SASHIMI (Workshop)
|June 14, 2018
PubMed
Summary

This study introduces a novel method for improving medical image registration accuracy in the presence of large pathologies. The approach maps pathological images to quasi-normal ones, enhancing registration precision for conditions like brain tumors.

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Area of Science:

  • Medical image analysis
  • Computational neuroimaging
  • Machine learning in medicine

Background:

  • Accurate medical image registration is crucial for diagnosis and treatment planning.
  • Large pathologies, such as brain tumors, pose significant challenges to traditional atlas-to-image registration.
  • Existing methods often struggle with accuracy in highly altered anatomical regions.

Purpose of the Study:

  • To develop an improved method for atlas-to-image registration that enhances accuracy in the presence of large pathologies.
  • To address the limitations of direct registration in pathological brain images.
  • To leverage deep learning for more robust medical image registration.

Main Methods:

  • A deep variational convolutional encoder-decoder network was employed to learn a mapping from pathological images to quasi-normal images.
  • The method estimates local mapping uncertainty using network inference statistics.
  • Uncertainty estimates are utilized to down-weight the image registration similarity measure in high-uncertainty regions.

Main Results:

  • The proposed method demonstrated improved accuracy in atlas-to-image registration for images with significant pathologies.
  • Performance was validated using synthetic brain tumor images.
  • The approach showed effectiveness on real-world data from the Brain Tumor Segmentation Challenge (BRATS 2015).

Conclusions:

  • The developed mapping approach effectively improves registration accuracy for pathological images.
  • Incorporating local mapping uncertainty enhances the robustness of the registration process.
  • This method offers a promising solution for neuroimaging analysis involving significant anatomical alterations.