Development of a population-based model of surface segmentation uncertainties for uncertainty-weighted deformable image registrations

Jian Wu, Martin J. Murphy, Elisabeth Weiss, William C. Sleeman, Jeffrey Williamson

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Purpose: To develop a population-based model of surface segmentation uncertainties for uncertainty-weighted surface-based deformable registrations. Methods: The contours of the prostate, the bladder, and the rectum were manually delineated by five observers on fan beam CT images of four prostate cancer patients. First, patient-specific representations of structure segmentation uncertainties were derived by determining the interobserver variability (i.e., standard deviation) of the structure boundary delineation. This was achieved by (1) generating an average structure surface mesh from the structure contours drawn by different observers, and (2) calculating three-dimensional standard deviation surface meshes (SDSMs) based on the perpendicular distances from the individual boundary surface meshes to the average surface mesh computed above. Then an average structure surface mesh was constructed to be the reference mesh for the population-based model. The average structure meshes of the other patients were deformably registered to the reference mesh. The calculated deformable vector fields were used to map the patient-specific SDSMs to the reference mesh to obtain the registered SDSMs. Finally, the population-based SDSM was derived by taking the average of the registered SDSMs in quadrature. Results: Population-based structure surface statistical models of the prostate, the bladder, and the rectum were created by mapping the patient-specific SDSMs to the population surface model. Graphical visualization indicates that the boundary uncertainties are dependent on anatomical location. Conclusions: The authors have developed and demonstrated a general method for objectively constructing surface maps of uncertainties derived from topologically complex structure boundary segmentations from multiple observers. The computed boundary uncertainties have significant spatial variations. They can be used as weighting factors for surface-based probabilistic deformable registration.

Original languageEnglish
Pages (from-to)607-614
Number of pages8
JournalMedical physics
Volume37
Issue number2
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Deformable registration
  • Population model
  • Segmentation uncertainty
  • Surface-based registration
  • Uncertainty model

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