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Using a deep learning approach for implanted seed detection on fluoroscopy images in prostate brachytherapy.

Andy Yuan1, Tarun Podder2, Jiankui Yuan2

  • 1Youngstown State University, Youngstown, United States.

Journal of Contemporary Brachytherapy
|March 27, 2023
PubMed
Summary

This study developed a deep learning model for automatic seed detection in prostate brachytherapy fluoroscopy images. The model achieved high accuracy, demonstrating potential for clinical application.

Keywords:
automatic seed identificationbrachytherapydeep learningprostate seed implant

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate brachytherapy involves implanting radioactive seeds for cancer treatment.
  • Accurate localization of these seeds is crucial for effective treatment planning and delivery.
  • Manual seed detection on fluoroscopy images can be time-consuming and prone to error.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automated detection of implanted seeds in prostate brachytherapy fluoroscopy images.
  • To improve the efficiency and accuracy of seed localization in clinical practice.

Main Methods:

  • Utilized 48 fluoroscopy images from patients undergoing permanent seed implant (PSI).
  • Applied pre-processing steps including bounding box encapsulation, dimension normalization, prostate region cropping, and image format conversion.
  • Employed a pre-trained Faster Region Convolutional Neural Network (R-CNN) for seed detection.
  • Evaluated model performance using leave-one-out cross-validation (LOOCV).

Main Results:

  • The deep learning model achieved high performance metrics across all cases.
  • Mean Average Precision (mAP) exceeded 0.91 in almost all cases.
  • Mean Average Recall (mAR) was above 0.9 for 83.3% of cases.
  • F1-scores consistently surpassed 0.91, with averaged results of 0.979 for mAP, 0.937 for mAR, and 0.957 for F1-score.

Conclusions:

  • The developed deep learning model demonstrates high accuracy in automatically detecting implanted seeds on fluoroscopy images.
  • Despite limitations in interpreting overlapping seeds, the model shows significant potential for enhancing prostate brachytherapy procedures.
  • Further research and development could refine the model for broader clinical adoption.