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Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction

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

Abstract

Purpose

In the context of analyzing neck vascular morphology, this work formulates and compares Mask R-CNN and U-Net-based algorithms to automatically segment the carotid artery (CA) and internal jugular vein (IJV) from transverse neck ultrasound (US).

Methods

US scans of the neck vasculature were collected to produce a dataset of 2439 images and their respective manual segmentations. Fourfold cross-validation was employed to train and evaluate Mask RCNN and U-Net models. The U-Net algorithm includes a post-processing step that selects the largest connected segmentation for each class. A Mask R-CNN-based vascular reconstruction pipeline was validated by performing a surface-to-surface distance comparison between US and CT reconstructions from the same patient.

Results

The average CA and IJV Dice scores produced by the Mask R-CNN across the evaluation data from all four sets were \(0.90\pm 0.08\) and \(0.88\pm 0.14\). The average Dice scores produced by the post-processed U-Net were \(0.81\pm 0.21\) and \(0.71\pm 0.23\), for the CA and IJV, respectively. The reconstruction algorithm utilizing the Mask R-CNN was capable of producing accurate 3D reconstructions with majority of US reconstruction surface points being within 2 mm of the CT equivalent.

Conclusions

On average, the Mask R-CNN produced more accurate vascular segmentations compared to U-Net. The Mask R-CNN models were used to produce 3D reconstructed vasculature with a similar accuracy to that of a manually segmented CT scan. This implementation of the Mask R-CNN network enables automatic analysis of the neck vasculature and facilitates 3D vascular reconstruction.

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Notes

  1. https://github.com/VASST/AIVascularSegmentation.

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Acknowledgements

We would like to acknowledge the NVIDIA GPU Grant held by Yiming Xiao and SHARCNET for their contribution to training the networks

Funding

This study was funded by Canadian Foundation for Innovation (20994), the Ontario Research Fund (IDCD), and the Canadian Institutes for Health Research (FDN 201409).

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Authors and Affiliations

  1. School of Biomedical Engineering, Western University, London, Canada

    Leah A. Groves

  2. Schulich School of Medicine and Dentistry, Western University, London, Canada

    Blake VanBerlo & Natan Veinberg

  3. Department of Anesthesia and Perioperative Medicine, Western University, London, Canada

    Abdulrahman Alboog

  4. Department of Medical Biophysics, School of Biomedical Engineering, Robarts Research, London, Canada

    Terry M. Peters & Elvis C. S. Chen

Authors
  1. Leah A. Groves
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  2. Blake VanBerlo
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  3. Natan Veinberg
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  4. Abdulrahman Alboog
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  5. Terry M. Peters
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  6. Elvis C. S. Chen
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Corresponding author

Correspondence to Leah A. Groves.

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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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Informed consent was obtained from all individual participants included in the study.

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Groves, L.A., VanBerlo, B., Veinberg, N. et al. Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction. Int J CARS 15, 1835–1846 (2020). https://doi.org/10.1007/s11548-020-02248-2

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  • DOI: https://doi.org/10.1007/s11548-020-02248-2

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