AUTOMATIC QUALITY ASSESSMENT OF TRANSPERINEAL ULTRASOUND IMAGES OF THE MALE PELVIC REGION, USING DEEP LEARNING

S. M. Camps, T. Houben, G. Carneiro, C. Edwards, M. Antico, M. Dunnhofer, E. G. H. J. Martens, J. A. Baeza, B. G. L. Vanneste, E. J. van Limbergen, P. H. N. de With, F. Verhaegen, D. Fontanarosa*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Web of Science)

Abstract

Ultrasound guidance is not in widespread use in prostate cancer radiotherapy workflows. This can be partially attributed to the need for image interpretation by a trained operator during ultrasound image acquisition. In this work, a one-class regressor, based on DenseNet and Gaussian processes, was implemented to automatically assess the quality of transperineal ultrasound images of the male pelvic region. The implemented deep learning approach was tested on 300 transperineal ultrasound images and it achieved a scoring accuracy of 94%, a specificity of 95% and a sensitivity of 92% with respect to the majority vote of 3 experts, which was comparable with the results of these experts. This is the first step toward a fully automatic workflow, which could potentially remove the need for ultrasound image interpretation and make real-time volumetric organ tracking in the radio- therapy environment using ultrasound more appealing. (C) 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)445-454
Number of pages10
JournalUltrasound in Medicine and Biology
Volume46
Issue number2
DOIs
Publication statusPublished - Feb 2020

Keywords

  • Transperineal ultrasound imaging
  • Deep learning
  • Prostate
  • Image-guided radiotherapy
  • Ultrasound
  • Radiotherapy
  • EXTERNAL-BEAM RADIOTHERAPY
  • ONE-CLASS CLASSIFICATION
  • INTRA-FRACTION MOTION
  • PROSTATE
  • GUIDANCE

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