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Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features

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

Abstract

Purpose

Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections.

Methods

A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification.

Results

The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image.

Conclusion

A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for detecting the laminae and facet joints in ultrasound images has been proposed. The system has the potential to assist the anesthesiologists in quickly finding the target plane for epidural steroid injections and facet joint injections.

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Acknowledgments

The authors would like to thank I. Hacihaliloglu and D. Tran for generously providing the implementations of their proposed methods. This study is jointly funded by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and Canadian Institutes of Health Research (CIHR). Conflict of interest The authors declare that they have no conflict of interest. Ethical standard All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent Informed consent was obtained from all individual participants included in the study.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada

    Mehran Pesteie, Purang Abolmaesumi, Hussam Al-Deen Ashab & Robert N. Rohling

  2. Department of Ultrasound, Women’s Hospital, Vancouver, BC, Canada

    Victoria A. Lessoway, Simon Massey & Vit Gunka

  3. Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada

    Robert N. Rohling

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  1. Mehran Pesteie
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  2. Purang Abolmaesumi
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  3. Hussam Al-Deen Ashab
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  4. Victoria A. Lessoway
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  5. Simon Massey
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  6. Vit Gunka
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  7. Robert N. Rohling
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Correspondence to Mehran Pesteie.

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Pesteie, M., Abolmaesumi, P., Ashab, H.AD. et al. Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features. Int J CARS 10, 901–912 (2015). https://doi.org/10.1007/s11548-015-1202-5

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  • DOI: https://doi.org/10.1007/s11548-015-1202-5

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