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Robust Unsupervised Image to Template Registration Without Image Similarity Loss

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Medical Image Learning with Limited and Noisy Data (MILLanD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14307))

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Abstract

Although a giant step forward has been made in medical images analysis thanks to deep learning, good results still require a lot of tedious and costly annotations. For image registration, unsupervised methods usually consider the training of a network using classical registration dissimilarity metrics. In this paper, we focus on the case of affine registration and show that this approach is not robust when the transform to estimate is large. We propose an unsupervised method for the training of an affine image registration network without using dissimilarity metrics and show that we are able to robustly register images even when the field of view is significantly different in the image.

S. Hachicha and C. Le—The first two authors contributed equally to this work.

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Acknowledgments

This work was supported by the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, by the “Projet Emergence" CNRS-INS2I APIDIFF, by the INSA BQR SALVE and by the France Life Imaging network (ANR-11-INBS-0006). Experiments were carried out using HPC resources from GENCI-IDRIS (AD011012544/AD011012589).

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Authors and Affiliations

  1. Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France, Lyon, France

    Slim Hachicha, Célia Le, Valentine Wargnier-Dauchelle & Michaël Sdika

Authors
  1. Slim Hachicha
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  2. Célia Le
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  3. Valentine Wargnier-Dauchelle
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  4. Michaël Sdika
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Corresponding author

Correspondence to Michaël Sdika .

Editor information

Editors and Affiliations

  1. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Zhiyun Xue

  2. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Sameer Antani

  3. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Ghada Zamzmi

  4. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Feng Yang

  5. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Sivaramakrishnan Rajaraman

  6. College of Information Sciences and Technology, Penn State University, University Park, PA, USA

    Sharon Xiaolei Huang

  7. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA

    Marius George Linguraru

  8. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Zhaohui Liang

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Hachicha, S., Le, C., Wargnier-Dauchelle, V., Sdika, M. (2023). Robust Unsupervised Image to Template Registration Without Image Similarity Loss. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_14

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  • DOI: https://doi.org/10.1007/978-3-031-44917-8_14

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-44917-8

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