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aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data

Author

Listed:
  • Christian J. Niedworok

    (MRC National Institute for Medical Research
    The Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London)

  • Alexander P. Y. Brown

    (MRC National Institute for Medical Research
    The Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London)

  • M. Jorge Cardoso

    (Translational Imaging Group, Centre for Medical Image Computing, University College London)

  • Pavel Osten

    (Cold Spring Harbor Laboratory)

  • Sebastien Ourselin

    (Translational Imaging Group, Centre for Medical Image Computing, University College London)

  • Marc Modat

    (Translational Imaging Group, Centre for Medical Image Computing, University College London)

  • Troy W. Margrie

    (MRC National Institute for Medical Research
    The Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London)

Abstract

The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain.

Suggested Citation

  • Christian J. Niedworok & Alexander P. Y. Brown & M. Jorge Cardoso & Pavel Osten & Sebastien Ourselin & Marc Modat & Troy W. Margrie, 2016. "aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11879
    DOI: 10.1038/ncomms11879
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    Cited by:

    1. Simon Weiler & Vahid Rahmati & Marcel Isstas & Johann Wutke & Andreas Walter Stark & Christian Franke & Jürgen Graf & Christian Geis & Otto W. Witte & Mark Hübener & Jürgen Bolz & Troy W. Margrie & Kn, 2024. "A primary sensory cortical interareal feedforward inhibitory circuit for tacto-visual integration," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    2. Dongsheng Xiao & Brandon J. Forys & Matthieu P. Vanni & Timothy H. Murphy, 2021. "MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Matthias Griebel & Dennis Segebarth & Nikolai Stein & Nina Schukraft & Philip Tovote & Robert Blum & Christoph M. Flath, 2023. "Deep learning-enabled segmentation of ambiguous bioimages with deepflash2," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Adam L Tyson & Charly V Rousseau & Christian J Niedworok & Sepiedeh Keshavarzi & Chryssanthi Tsitoura & Lee Cossell & Molly Strom & Troy W Margrie, 2021. "A deep learning algorithm for 3D cell detection in whole mouse brain image datasets," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-17, May.
    5. Jan C. Frankowski & Alexa Tierno & Shreya Pavani & Quincy Cao & David C. Lyon & Robert F. Hunt, 2022. "Brain-wide reconstruction of inhibitory circuits after traumatic brain injury," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

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