Automated prostate segmentation in whole-body MRI scans for epidemiological studies

Mohamad Habes, Thilo Schiller, Christian Rosenberg, Martin Burchardt, Wolfgang Hoffmann

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The whole prostatic volume (PV) is an important indicator for benign prostate hyperplasia. Correlating the PV with other clinical parameters in a population-based prospective cohort study (SHIP-2) requires valid prostate segmentation in a large number of whole-body MRI scans. The axial proton density fast spin echo fat saturated sequence is used for prostate screening in SHIP-2. Our automated segmentation method is based on support vector machines (SVM). We used three-dimensional neighborhood information to build classification vectors from automatically generated features and randomly selected 16 MR examinations for validation. The Hausdorff distance reached a mean value of 5.048 ± 2.413, and a mean value of 5.613 ± 2.897 compared to manual segmentation by observers A and B. The comparison between volume measurement of SVM-based segmentation and manual segmentation of observers A and B depicts a strong correlation resulting in Spearman's rank correlation coefficients (ρ) of 0.936 and 0.859, respectively. Our automated methodology based on SVM for prostate segmentation can segment the prostate in WBI scans with good segmentation quality and has considerable potential for integration in epidemiological studies.

Original languageEnglish (US)
Pages (from-to)5899-5915
Number of pages17
JournalPhysics in Medicine and Biology
Volume58
Issue number17
DOIs
StatePublished - Sep 7 2013
Externally publishedYes

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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