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

  • Ahmed Z. Alsinan
  • Vishal M. Patel
  • Ilker HacihalilogluEmail author
Original Article
  • 12 Downloads

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.

Keywords

Orthopedic surgery Segmentation Ultrasound Bone Deep learning 

Notes

Funding

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

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringRutgers UniversityPiscatawayUSA
  2. 2.Department of Biomedical EngineeringRutgers UniversityPiscatawayUSA
  3. 3.Rutgers University Robert Wood Johnson Medical SchoolNew BrunswickUSA

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