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Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions.

Xinzhou Li1,2, Adam S Young1, Steven S Raman1

  • 1Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.

International Journal of Computer Assisted Radiology and Surgery
|July 18, 2020
PubMed
Summary

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This study introduces an AI algorithm for precise MRI-guided needle tracking during interventions. The Mask Region Proposal-Based Convolutional Neural Network (R-CNN) accurately identifies needle position and orientation in real-time, improving procedural safety.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Interventional Radiology

Background:

  • Accurate needle tracking is crucial for MRI-guided percutaneous interventions.
  • Passive tracking methods face challenges due to signal void variations in MR images.
  • Developing automated, reliable needle tracking is essential for enhancing procedural accuracy and safety.

Purpose of the Study:

  • To develop an automatic needle tracking algorithm for MRI-guided interventions.
  • To leverage the Mask Region Proposal-Based Convolutional Neural Network (Mask R-CNN) for needle tracking.
  • To improve the accuracy and reliability of needle localization and orientation detection in real-time MRI.

Main Methods:

  • Adapted and trained Mask R-CNN using 250 intra-procedural MR images from prostate biopsies and 180 real-time ex vivo images.
Keywords:
Convolutional neural networkDeep learningDevice trackingInterventional MRINeedle featureReal-time MRI

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  • Utilized segmentation masks to extract needle tip location and axis orientation.
  • Validated the algorithm on 208 intra-procedural images and 3 real-time MRI datasets, comparing results with human annotations.

    • Main Results:

    • Achieved median tip localization error (dxy) of 0.71 mm and orientation error (dθ) of 1.28° in prostate biopsy datasets.
    • Demonstrated consistent real-time tracking in ex vivo datasets with median errors ranging from 0.90 mm to 1.31 mm for dxy and 0.91° to 1.9° for dθ.
    • Processed images in 75 ms, indicating real-time performance capabilities.

    Conclusions:

    • The Mask R-CNN-based algorithm accurately tracks needle tip and axis in diverse MRI conditions (in vivo and ex vivo).
    • The algorithm achieves pixel-level tracking accuracy in real time.
    • This automated tracking system shows significant potential to assist and enhance MRI-guided percutaneous interventions.