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An Automatic Biopsy Needle Detection and Segmentation on Ultrasound Images Using a Convolutional Neural Network.

Agata Wijata1, Jacek Andrzejewski1, Bartłomiej Pyciński1

  • 1Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.

Ultrasonic Imaging
|June 28, 2021
PubMed
Summary

This study introduces an automatic needle detection method using a CNN for ultrasound-guided biopsies. The system accurately identifies needles in breast cancer images, improving diagnostic efficiency.

Keywords:
convolutional neural networkscore-needle biopsyneedle detectionneedle segmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Ultrasound-guided core needle biopsy requires precise needle visualization for accurate diagnosis.
  • Automated needle detection can enhance procedural efficiency and diagnostic accuracy.

Purpose of the Study:

  • To develop a fully automatic method for detecting core needles in 2D ultrasound images using a Convolutional Neural Network (CNN).

Main Methods:

  • A CNN architecture with an Adaptive Moment Estimation optimizer was employed for needle detection.
  • The Radon transform was utilized to precisely locate the needle within the ultrasound images.
  • The model was trained and validated on 619 2D images from 91 breast cancer cases.

Main Results:

  • The model achieved a high weighted Jaccard Index of 0.986 and an F1 Score of 0.768.
  • The average Root Mean Square Error (RMSE) for angle detection was 3.73°.
  • Needle detection was achieved in an average of 21.6 ms per frame, outperforming existing methods.

Conclusions:

  • The proposed CNN-based method offers a robust and efficient solution for automatic core needle detection in ultrasound imaging.
  • This technology has the potential to significantly improve the accuracy and speed of ultrasound-guided biopsies.
  • The system demonstrates superior performance compared to current solutions in terms of detection accuracy and speed.