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Automated multi-atlas segmentation of gluteus maximus from Dixon and T1-weighted magnetic resonance images

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

To design, develop and evaluate an automated multi-atlas method for segmentation and volume quantification of gluteus maximus from Dixon and T1-weighted images.

Materials and methods

The multi-atlas segmentation method uses an atlas library constructed from 15 Dixon MRI scans of healthy subjects. A non-rigid registration between each atlas and the target, followed by majority voting label fusion, is used in the segmentation. We propose a region of interest (ROI) to standardize the measurement of muscle bulk. The method was evaluated using the dice similarity coefficient (DSC) and the relative volume difference (RVD) as metrics, for Dixon and T1-weighted target images.

Results

The mean(± SD) DSC was 0.94 ± 0.01 for Dixon images, while 0.93 ± 0.02 for T1-weighted. The RVD between the automated and manual segmentation had a mean(± SD) value of 1.5 ± 4.3% for Dixon and 1.5 ± 4.8% for T1-weighted images. In the muscle bulk ROI, the DSC was 0.95 ± 0.01 and the RVD was 0.6 ± 3.8%.

Conclusion

The method allows an accurate fully automated segmentation of gluteus maximus for Dixon and T1-weighted images and provides a relatively accurate volume measurement in shorter times (~ 20 min) than the current gold-standard manual segmentations (2 h). Visual inspection of the segmentation would be required when higher accuracy is needed.

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Acknowledgements

This research study was funded by The Maurice Hatter Foundation, the RNOH Charity, the Rosetrees Trust and the Stoneygate Trust and supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre.

Funding

This research study was funded by The Maurice Hatter Foundation, the RNOH Charity, the Rosetrees Trust and the Stoneygate Trust and supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre.

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Authors and Affiliations

  1. Royal National Orthopaedic Hospital (RNOH), Brockley Hill, Stanmore, HA7 4LP, Middlesex, UK

    Martin A. Belzunce, Johann Henckel, Anastasia Fotiadou, Anna Di Laura & Alister Hart

  2. Institute of Orthopaedics and Musculoskeletal Science, University College London, Stanmore, HA7 4LP, UK

    Alister Hart

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  1. Martin A. Belzunce
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  2. Johann Henckel
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  3. Anastasia Fotiadou
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  4. Anna Di Laura
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Contributions

MAB: Study conception and design, acquisition of data, analysis and interpretation of data, drafting of manuscript, critical revision. JH: Study conception and design, acquisition of data, analysis and interpretation of data, critical revision. AF: Acquisition of data, critical revision. ADL: Study conception and design, Acquisition of data, critical revision. AH: Study conception and design, acquisition of data, analysis and interpretation of data, critical revision.

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Correspondence to Martin A. Belzunce.

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All procedures performed in this work involving human participants were approved by the local Institutional Review Board. All subjects have given written informed consent.

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Belzunce, M.A., Henckel, J., Fotiadou, A. et al. Automated multi-atlas segmentation of gluteus maximus from Dixon and T1-weighted magnetic resonance images. Magn Reson Mater Phy 33, 677–688 (2020). https://doi.org/10.1007/s10334-020-00839-3

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  • DOI: https://doi.org/10.1007/s10334-020-00839-3

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