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A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

Kibrom Berihu Girum1,2, Alain Lalande3,4, Raabid Hussain3

  • 1ImViA Laboratory, University of Burgundy, Batiment I3M, 64b rue sully, 21000, Dijon, France. kibrom-berihu_girum@etu.u-bourgogne.fr.

International Journal of Computer Assisted Radiology and Surgery
|July 22, 2020
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Summary

This study introduces an automatic deep learning method for detecting the clinical target volume (CTV) in prostate brachytherapy using transrectal ultrasound (TRUS) images. The approach enhances accuracy and efficiency in image-guided interventions.

Keywords:
Convolutional neural networksImage segmentationImage-guided brachytherapyIntraoperativeShape modelsTransrectal ultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate detection of the clinical target volume (CTV) is crucial for effective prostate brachytherapy.
  • Current methods for CTV detection in transrectal ultrasound (TRUS) guided interventions can be time-consuming and lack reproducibility.
  • Developing automated solutions is essential for improving clinical workflow and patient outcomes.

Purpose of the Study:

  • To develop and evaluate a robust, automatic deep learning method for CTV boundary detection in intraoperative TRUS images.
  • To improve the speed and reproducibility of interventions in permanent prostate brachytherapy.
  • To leverage both low-level image features and high-level prior shape information for enhanced detection accuracy.

Main Methods:

  • A multi-task deep learning approach was employed for automatic prostate CTV boundary detection.
  • The method incorporates a channel-wise feature calibration strategy for low-level feature extraction.
  • It utilizes learning-based prior knowledge modeling for CTV shape reconstruction from sampled boundary surface coordinates.

Main Results:

  • The method was validated on a dataset of 145 patients undergoing permanent prostate brachytherapy.
  • Achieved a mean accuracy of [Formula: see text] and a mean surface distance error of [Formula: see text].
  • Outperformed previous methods by over 7% in Dice similarity coefficient and reduced 3D Hausdorff distance error by 6.9 mm.

Conclusions:

  • Shape model-based deep learning methods show significant potential for accurate CTV segmentation in ultrasound-guided interventions.
  • Integrating low-level features and prior shape knowledge with channel-wise feature calibration improves deep learning performance in medical image segmentation.
  • The proposed method offers an efficient and accurate solution for CTV detection in prostate brachytherapy.