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Homologous point transformer for multi-modality prostate image registration.

Alexander Ruchti1, Alexander Neuwirth1, Allison K Lowman2

  • 1Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, WI, United States.

Peerj. Computer Science
|December 19, 2022
PubMed
Summary

This study introduces a new deep learning method for aligning medical images from different scans, like radiology and pathology. The transformer-based pipeline accurately registers prostate images by identifying homologous points, outperforming current methods.

Keywords:
Control pointsDeep learningMedical imagingRegistrationTransformer

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Area of Science:

  • Medical imaging
  • Computer vision
  • Machine learning

Background:

  • Image registration aligns images in the same coordinate space, crucial for comparing multi-modal or multi-parametric medical scans.
  • Cross-modality registration, aligning images from different scanning techniques, presents a significant challenge in medical image analysis.

Purpose of the Study:

  • To develop and evaluate a transformer-based deep learning pipeline for cross-modality radiology-pathology image registration of human prostate samples.
  • To improve upon existing methods by predicting homologous points instead of transform parameters.

Main Methods:

  • A novel transformer-based deep learning pipeline was designed for cross-modality image registration.
  • The pipeline focuses on predicting homologous points between radiology and pathology images of prostate samples.
  • Performance was evaluated against state-of-the-art automatic registration pipelines.

Main Results:

  • The homologous point registration pipeline achieved superior accuracy compared to the current state-of-the-art.
  • The method demonstrated a better average control point deviation.
  • The pipeline successfully registered images without the need for masked MR images.

Conclusions:

  • The proposed transformer-based pipeline offers a more accurate approach to cross-modality prostate image registration.
  • This method's ability to avoid masked MR images suggests potential applicability to other organs and partial tissue samples.
  • The homologous point prediction strategy represents a significant advancement in medical image registration techniques.