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Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN

International Journal of Computer Assisted Radiology and Surgery volume 14, pages 775–783 (2019)Cite this article

Abstract

Purpose

Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However, interpretation of the collected bone US data is difficult due to high levels of noise, various imaging artifacts, and bone surfaces response appearing several millimeters (mm) in thickness. For US-guided CAOS procedures, it is an essential objective to have a segmentation mechanism, that is both robust and computationally inexpensive.

Method

In this paper, we present our development of a convolutional neural network-based technique for segmentation of bone surfaces from in vivo US scans. The novelty of our proposed design is that it utilizes fusion of feature maps and employs multi-modal images to abate sensitivity to variations caused by imaging artifacts and low intensity bone boundaries. B-mode US images, and their corresponding local phase filtered images are used as multi-modal inputs for the proposed fusion network. Different fusion architectures are investigated for fusing the B-mode US image and the local phase features.

Results

The proposed methods was quantitatively and qualitatively evaluated on 546 in vivo scans by scanning 14 healthy subjects. We achieved an average F-score above 95% with an average bone surface localization error of 0.2 mm. The reported results are statistically significant compared to state-of-the-art.

Conclusions

Reported accurate and robust segmentation results make the proposed method promising in CAOS applications. Further extensive validations are required in order to fully understand the clinical utility of the proposed 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. Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA

    Ahmed Z. Alsinan & Vishal M. Patel

  2. Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA

    Ilker Hacihaliloglu

  3. Rutgers University Robert Wood Johnson Medical School, New Brunswick, NJ, USA

    Ilker Hacihaliloglu

Authors
  1. Ahmed Z. Alsinan
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  2. Vishal M. Patel
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  3. Ilker Hacihaliloglu
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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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Alsinan, A.Z., Patel, V.M. & Hacihaliloglu, I. Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN. Int J CARS 14, 775–783 (2019). https://doi.org/10.1007/s11548-019-01934-0

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  • DOI: https://doi.org/10.1007/s11548-019-01934-0

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