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Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN

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

Abstract

Purpose

Automatic bone surfaces segmentation is one of the fundamental tasks of ultrasound (US)-guided computer-assisted orthopedic surgery procedures. However, due to various US imaging artifacts, manual operation of the transducer during acquisition, and different machine settings, many existing methods cannot deal with the large variations of the bone surface responses, in the collected data, without manual parameter selection. Even for fully automatic methods, such as deep learning-based methods, the problem of dataset bias causes networks to perform poorly on the US data that are different from the training set.

Methods

In this work, an intensity-invariant convolutional neural network (CNN) architecture is proposed for robust segmentation of bone surfaces from US data obtained from two different US machines with varying acquisition settings. The proposed CNN takes US image as input and simultaneously generates two intermediate output images, denoted as local phase tensor (LPT) and global context tensor (GCT), from two branches which are invariant to intensity variations. LPT and GCT are fused to generate the final segmentation map. In the training process, the LPT network branch is supervised by precalculated ground truth without manual annotation.

Results

The proposed method is evaluated on 1227 in vivo US scans collected using two US machines, including a portable handheld ultrasound scanner, by scanning various bone surfaces from 28 volunteers. Validation of proposed method on both US machines not only shows statistically significant improvements in cross-machine segmentation of bone surfaces compared to state-of-the-art methods but also achieves a computation time of 30 milliseconds per image, \(98.5\%\) improvement over state-of-the-art.

Conclusion

The encouraging results obtained in this initial study suggest that the proposed method is promising enough for further evaluation. Future work will include extensive validation of the method on new US data collected from various machines using different acquisition settings. We will also evaluate the potential of using the segmented bone surfaces as an input to a point set-based registration method.

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Funding

This work was supported in part by 2017 North American Spine Society Young Investigator Award.

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Authors and Affiliations

  1. Johns Hopkins University, Baltimore, USA

    Puyang Wang & Vishal M. Patel

  2. Rutgers New Jersey Medical School, Newark, USA

    Michael Vives

  3. Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, 08854, USA

    Ilker Hacihaliloglu

Authors
  1. Puyang Wang
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  2. Michael Vives
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  3. Vishal M. Patel
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  4. Ilker Hacihaliloglu
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Corresponding author

Correspondence to Ilker Hacihaliloglu.

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The authors declare that they have no conflict of interest.

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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.

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Wang, P., Vives, M., Patel, V.M. et al. Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN. Int J CARS 15, 1127–1135 (2020). https://doi.org/10.1007/s11548-020-02184-1

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  • DOI: https://doi.org/10.1007/s11548-020-02184-1

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