Radiological Physics and Technology

, Volume 11, Issue 4, pp 434–444 | Cite as

Feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy

  • Kenta Ninomiya
  • Hidetaka ArimuraEmail author
  • Motoki Sasahara
  • Yudai Kai
  • Taka-aki Hirose
  • Saiji Ohga


This study aimed to investigate the feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy. The relationships between the reference centroids of prostate regions (CPRs) (prostate locations) and anatomical feature points were explored, and the most feasible anatomical feature points were selected based on the smallest location estimation errors of CPRs and the largest Dice’s similarity coefficient (DSC) between the reference and extracted prostates. The reference CPRs were calculated according to reference prostate contours determined by radiation oncologists. Five anatomical feature points were manually determined on a prostate, bladder, and rectum in a sagittal plane of a planning computed tomography image for each case. The CPRs were estimated using three machine learning architectures [artificial neural network, random forest, and support vector machine (SVM)], which learned the relationships between the reference CPRs and anatomical feature points. The CPRs were applied for placing a prostate probabilistic atlas at the coordinates and extracting prostate regions using a Bayesian delineation framework. The average estimation errors without and with SVM using three feature points, which indicated the best performance, were 5.6 ± 3.7 mm and 1.8 ± 1.0 mm, respectively (the smallest error) (p < 0.001). The average DSCs without and with SVM using the three feature points were 0.69 ± 0.13 and 0.82 ± 0.055, respectively (the highest DSC) (p < 0.001). The anatomical feature points may be feasible for the estimation of prostate locations, which can be applied to the general Bayesian delineation frameworks for prostate cancer radiotherapy.


Anatomical feature points Prostate location Machine learning Probabilistic atlas Bayesian inference Prostate cancer radiotherapy 


Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Human rights

This study was approved by the Institutional Review Board of the Kyushu University Hospital.

Animal rights

This article does not include other studies using animal models.


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Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2018

Authors and Affiliations

  1. 1.Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
  2. 2.Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical SciencesKyushu UniversityFukuokaJapan
  3. 3.Kumamoto University HospitalKumamotoJapan
  4. 4.Kyushu University HospitalFukuokaJapan
  5. 5.Department of Clinical Radiology, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan

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