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Related Experiment Video

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Automated machine learning (AutoML)-based surface registration methodology for image-guided surgical navigation

Hakje Yoo1, Taeyong Sim2

  • 1Korea University Research Institute for Medical Bigdata Science, College of Medicine, Korea University, Seoul, Republic of Korea.

Medical Physics
|May 11, 2022
PubMed
Summary

Automated machine learning (AutoML)-based surface registration improves accuracy in image-guided surgery. This method enhances precision while retaining the safety and speed of traditional surface registration techniques.

Keywords:
Bayesian optimizationautomated machine learningimage-guided surgeryiterative closest pointsurface registration

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Area of Science:

  • Medical Imaging
  • Computer-Aided Surgery
  • Machine Learning

Background:

  • Surface registration is a key technique in image-guided surgery, offering safety and speed.
  • However, traditional surface registration methods often suffer from low accuracy, limiting their clinical utility.

Purpose of the Study:

  • To introduce an automated machine learning (AutoML)-based surface registration method.
  • The goal is to significantly enhance the accuracy of image-guided surgical navigation systems.

Main Methods:

  • A neural network model extracts facial point-clouds from CT data, matching passive probe optical tracking system (OTS) information.
  • Iterative Closest Point (ICP) algorithm calculates Target Registration Error (TRE) using the extracted point-cloud and OTS data.
  • Bayesian optimization with expected improvement automatically optimizes hyperparameters for both the neural network and ICP algorithm, improving registration accuracy.

Main Results:

  • The proposed method achieved an average TRE of 0.939 ± 0.375 mm in soft phantoms, a 57.8% improvement over conventional methods (2.227 ± 0.193 mm).
  • Further evaluation showed average TREs of 0.767 ± 0.132 mm for the proposed method versus 2.615 ± 0.378 mm for the conventional method.
  • Clinical applicability was demonstrated in a healthy adult, validating the method's real-world potential.

Conclusions:

  • The developed AutoML-based surface registration significantly improves accuracy.
  • This advancement maintains the inherent safety and efficiency advantages of surface registration techniques.
  • The findings support the integration of AutoML for enhanced precision in surgical navigation.