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Spine labeling in MRI via regularized distribution matching

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

This study investigates an efficient (nearly real-time) two-stage spine labeling algorithm that removes the need for an external training while being applicable to different types of MRI data and acquisition protocols.

Methods

Based solely on the image being labeled (i.e., we do not use training data), the first stage aims at detecting potential vertebra candidates following the optimization of a functional containing two terms: (i) a distribution-matching term that encodes contextual information about the vertebrae via a density model learned from a very simple user input, which amounts to a point (mouse click) on a predefined vertebra; and (ii) a regularization constraint, which penalizes isolated candidates in the solution. The second stage removes false positives and identifies all vertebrae and discs by optimizing a geometric constraint, which embeds generic anatomical information on the interconnections between neighboring structures. Based on generic knowledge, our geometric constraint does not require external training.

Results

We performed quantitative evaluations of the algorithm over a data set of 90 mid-sagittal MRI images of the lumbar spine acquired from 45 different subjects. To assess the flexibility of the algorithm, we used both T1- and T2-weighted images for each subject. A total of 990 structures were automatically detected/labeled and compared to ground-truth annotations by an expert. On the T2-weighted data, we obtained an accuracy of 91.6% for the vertebrae and 89.2% for the discs. On the T1-weighted data, we obtained an accuracy of 90.7% for the vertebrae and 88.1% for the discs.

Conclusion

Our algorithm removes the need for external training while being applicable to different types of MRI data and acquisition protocols. Based on the current testing data, a subject-specific model density and generic anatomical information, our method can achieve competitive performances when applied to T1- and T2-weighted MRI images.

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Notes

  1. Typically, spine MRI studies contain more than 100 images.

  2. Our meta-analysis of accuracy is not a direct comparison between the methods because the used data sets are different, and might have various levels of difficulties. For instance, the CT data set in [29] contains several unusual appearances (e.g., abnormal spine curvature), and undergo large variations in the field of view, image noise and resolutions.

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Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Deparment of Medical Imaging, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Toronto, ON, M4N 3M5, Canada

    Seyed-Parsa Hojjat

  2. Ecole de Technologie Superieure (ETS), 1100 Rue Notre-Dame O, Montreal, QC, H3C 1K3, Canada

    Ismail Ayed

  3. Department of Medical Imaging, University of Western Ontario, 268 Grosvenor St, London, ON, N6A 4V2, Canada

    Gregory J. Garvin

  4. Department of Radiology and Diagnostic Imaging, University of Alberta, 3A5.039, 8440 - 112 St NW, Edmonton, AB, Canada

    Kumaradevan Punithakumar

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  1. Seyed-Parsa Hojjat
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  2. Ismail Ayed
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  4. Kumaradevan Punithakumar
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Corresponding author

Correspondence to Ismail Ayed.

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Conflict of interest

The author(s) declare that they have no competing interests.

Ethical approval

The study was approved by the University of Western Ontario Research Ethics Board.

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Hojjat, SP., Ayed, I., Garvin, G.J. et al. Spine labeling in MRI via regularized distribution matching. Int J CARS 12, 1911–1922 (2017). https://doi.org/10.1007/s11548-017-1651-0

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  • DOI: https://doi.org/10.1007/s11548-017-1651-0

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