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Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estimation in COPD patients

  • Max BlendowskiEmail author
  • Mattias P. Heinrich
Original Article

Abstract

Purpose

Deep convolutional neural networks in their various forms are currently achieving or outperforming state-of-the-art results on several medical imaging tasks. We aim to make these developments available to the so far unsolved task of accurate correspondence finding—especially with regard to image registration.

Methods

We propose a two-step hybrid approach to make deep learned features accessible to a discrete optimization-based registration method. In a first step, in order to extract expressive binary local descriptors, we train a deep network architecture on a patch-based landmark retrieval problem as auxiliary task. As second step at runtime within a MRF-regularised dense displacement sampling, their binary nature enables highly efficient similarity computations, thus making them an ideal candidate to replace the so far used handcrafted local feature descriptors during the registration process.

Results

We evaluate our approach on finding correspondences between highly non-rigidly deformed lung CT scans from different breathing states. Although the CNN-based descriptors excell at an auxiliary learning task for finding keypoint correspondences, self-similarity-based descriptors yield more accurate registration results. However, a combination of both approaches turns out to generate the most robust features for registration.

Conclusion

We present a three-dimensional framework for large lung motion estimation based on the combination of CNN-based and handcrafted descriptors efficiently employed in a discrete registration method. Achieving best results by combining learned and handcrafted features encourages further research in this direction.

Keywords

Deep learning Image registration Discrete optimization Hamming distance 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no relevant conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Statement of informed consent was not applicable since the manuscript does not contain any participants’ data.

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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckGermany

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