Automated Computed Tomography-Ultrasound Cross-Modality 3-D Contouring Algorithm for Prostate

Denis Ermacora, Silvia Pesente, Francesco Pascoli, Sebastian Raducci, Rudy Mauro, Imad Abu Rumeileh, Frank Verhaegen, Davide Fontanarosa

Research output: Contribution to journalArticlepeer-review

Abstract

A novel fully automated algorithm is introduced for 3-D cross-modality image segmentation of the prostate, based on the simultaneous use of co-registered computed tomography (CT) and 3-D ultrasound (US) images. By use of a Gabor feature detector, the algorithm can outline in three dimensions and in cross-modality the prostate, and it can be trained and optimized on specific patient populations. We applied it to 16 prostate cancer patients and evaluated the conformity between the automatically segmented prostate contours and the contours manually outlined by an experienced physician, on the CT-US fusion, using the mean distance to conformity (MDC) index. When only the CT scans were used, the average MDC value was 4.5 ± 1.7 mm (maximum value = 9.0 mm). When the US scans also were considered, the mean ± standard deviation was reduced to 3.9 ± 0.7 mm (maximum value = 5.5 mm). The cross-modality approach acted on all the largest distance values, reducing them to acceptable discrepancies.

Original languageEnglish
Pages (from-to)2646-2662
Number of pages17
JournalUltrasound in Medicine and Biology
Volume41
Issue number10
DOIs
Publication statusPublished - 2015

Keywords

  • Automated image segmentation
  • Computed tomography imaging
  • Cross-modality
  • Image-guided radiation therapy
  • Radiotherapy
  • Ultrasound imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Biophysics

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