Targeted prostate biopsy using statistical image analysis

Yiqiang Zhan, Dinggang Shen, Jianchao Zeng, Leon Sun, Gabor Fichtinger, Judd Moul, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies.

Original languageEnglish (US)
Pages (from-to)779-788
Number of pages10
JournalIEEE transactions on medical imaging
Issue number6
StatePublished - Jun 2007
Externally publishedYes


  • Biopsy optimization
  • Prostate cancer
  • Spatial normalization
  • Statistical image analysis

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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