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Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network

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Biomedical Image Registration (WBIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13386))

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Abstract

Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.

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References

  1. Niethammer, M., et al.: Geometric metamorphosis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 639–646. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23629-7_78

    Chapter  Google Scholar 

  2. Rekik, I., Li, G., Wu, G., Lin, W., Shen, D.: Prediction of infant MRI appearance and anatomical structure evolution using sparse patch-based metamorphosis learning framework. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds.) Patch-MI 2015. LNCS, vol. 9467, pp. 197–204. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-28194-0_24

    Chapter  Google Scholar 

  3. Chen, K., Derksen, A., Heldmann, S., Hallmann, M., Berkels, B.: Deformable image registration with automatic non-correspondence detection. In: Aujol, J.-F., Nikolova, M., Papadakis, N. (eds.) SSVM 2015. LNCS, vol. 9087, pp. 360–371. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18461-6_29

    Chapter  Google Scholar 

  4. Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15(4), 622–639 (2011) https://doi.org/10.1016/j.media.2010.07.002

  5. Krüger, J., Schultz, S., Handels, H., Ehrhardt, J.: Registration with probabilistic correspondences-accurate and robust registration for pathological and inhomogeneous medical data. Comput. Vis. Image Underst. 190 (2020). https://doi.org/10.1016/j.cviu.2019.102839

  6. Andresen, J., Kepp, T., Ehrhardt, J., von der Burchard, C., Roider, J., Handels, H.: Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies. Int. J. CARS 17, 699–710 (2022). https://doi.org/10.1007/s11548-022-02577-4

  7. Sedghi, A., Kapur, T., Luo, J., Mousavi, P., Wells, W.M.: Probabilistic image registration via deep multi-class classification: characterizing uncertainty. In: Greenspan, H., et al. (eds.) CLIP/UNSURE -2019. LNCS, vol. 11840, pp. 12–22. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32689-0_2

    Chapter  Google Scholar 

  8. Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration - a deep learning approach. NeuroImage 158, 378–396 (2017)

    Article  Google Scholar 

  9. Sentker, T., Madesta, F., Werner, R.: GDL-FIRE\(^\text{4D }\): deep learning-based fast 4D CT image registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 765–773. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_86

    Chapter  Google Scholar 

  10. Zhou, T., Krähenbühl, P., Aubry, M., Huang, Q., Efro, A.A.: Learning dense correspondence via 3D-guided cycle consistency. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 117–126 (2016)

    Google Scholar 

  11. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany

    Julia Andresen, Timo Kepp, Jan Ehrhardt & Heinz Handels

  2. German Research Center for Artificial Intelligence, Lübeck, Germany

    Jan Ehrhardt & Heinz Handels

  3. Department of Ophthalmology, Christian-Albrechts-University of Kiel, Kiel, Germany

    Claus von der Burchard & Johann Roider

Authors
  1. Julia Andresen
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  2. Timo Kepp
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  3. Jan Ehrhardt
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  4. Claus von der Burchard
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  5. Johann Roider
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  6. Heinz Handels
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Corresponding author

Correspondence to Julia Andresen .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Alessa Hering

  2. Helmholtz Center Munich, Neuherberg, Germany

    Julia Schnabel

  3. University of Virginia, Charlottesville, VA, USA

    Miaomiao Zhang

  4. CONICET, Universidad Nacional del Litoral, Santa Fe, Argentina

    Enzo Ferrante

  5. Universität zu Lübeck, Lübeck, Germany

    Mattias Heinrich

  6. GALILEO, Technical University of Munich, Munich, Germany

    Daniel Rueckert

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Andresen, J., Kepp, T., Ehrhardt, J., von der Burchard, C., Roider, J., Handels, H. (2022). Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham. https://doi.org/10.1007/978-3-031-11203-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-11203-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11202-7

  • Online ISBN: 978-3-031-11203-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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