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Related Experiment Video

Updated: Nov 11, 2025

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Automatic needle detection using improved random sample consensus in CT image-guided lung interstitial brachytherapy.

Yongnan Zheng1, Shan Jiang1, Zhiyong Yang1

  • 1School of Mechanical Engineering, Tianjin University, Tianjin, China.

Journal of Applied Clinical Medical Physics
|March 25, 2021
PubMed
Summary
This summary is machine-generated.

This study presents an automated method for detecting needles in CT images, crucial for lung brachytherapy. The technique accurately assesses needle placement and optimizes radiation dose distribution.

Keywords:
computed tomographyimproved random sample consensuslung brachytherapyneedle localization

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Computational Anatomy

Background:

  • Image-guided brachytherapy requires precise needle placement for effective cancer treatment.
  • Accurate assessment of needle position is critical for dose distribution optimization in lung interstitial brachytherapy.

Purpose of the Study:

  • To develop an automated method for detecting needles in CT images.
  • To enhance needle placement assessment and dose distribution optimization in image-guided lung interstitial brachytherapy.

Main Methods:

  • A modified random sample consensus algorithm combined with principal component analysis for rapid and robust needle shaft detection.
  • Needle tip determination by tracing intensities along the detected axis.
  • Simultaneous segmentation of multiple needles within a single slice using successive inliers deletion.

Main Results:

  • Simulation data demonstrated significantly higher segmentation efficiency compared to original random sample consensus, maintaining submillimeter accuracy.
  • Physical phantom experiments showed segmentation accuracy dependent on needle insertion depth.
  • Validation in permanent lung brachytherapy images confirmed the algorithm's applicability, with manual segmentation serving as a comparison.

Conclusions:

  • The developed method achieves mean errors within 2° for needle orientation and 1 mm for endpoint determination.
  • The average segmentation time per needle is remarkably fast at 0.238 seconds.
  • This automated approach offers a promising tool for improving precision in lung brachytherapy procedures.