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A deep learning framework for unsupervised affine and deformable image registration.

Bob D de Vos1, Floris F Berendsen2, Max A Viergever1

  • 1Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.

Medical Image Analysis
|December 23, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep Learning Image Registration (DLIR) framework for unsupervised medical image alignment. DLIR trains convolutional neural networks (ConvNets) without example data, achieving comparable performance to traditional methods but significantly faster.

Keywords:
Affine image registrationCardiac cine MRIChest CTDeep learningDeformable image registrationUnsupervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Image registration is crucial for medical image analysis.
  • Deep learning, specifically convolutional neural networks (ConvNets), shows promise for image registration.
  • Supervised training of ConvNets for registration requires difficult-to-obtain example registrations.

Purpose of the Study:

  • To propose a Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable medical image registration.
  • To enable training of ConvNets for image registration without predefined examples, enhancing convenience.
  • To develop flexible ConvNet designs for various registration tasks and a coarse-to-fine registration approach.

Main Methods:

  • The DLIR framework trains ConvNets using image similarity principles, akin to conventional intensity-based registration.
  • Flexible ConvNet architectures are designed for both affine and deformable image registration.
  • Multiple ConvNets are stacked to create a larger architecture for coarse-to-fine registration.

Main Results:

  • The DLIR framework demonstrates performance comparable to conventional image registration methods.
  • The proposed framework achieves registration significantly faster than traditional approaches (several orders of magnitude).
  • Successful registration was shown for cardiac cine MRI and chest CT datasets.

Conclusions:

  • The DLIR framework offers an effective and efficient unsupervised approach for medical image registration using deep learning.
  • This method circumvents the need for example registrations, simplifying the training process for ConvNets.
  • DLIR provides a powerful tool for accelerating medical image analysis tasks while maintaining accuracy.