Development of a population-based model of surface segmentation uncertainties for uncertainty-weighted deformable image registrations.
OBJECTIVE
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.
Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298, USA. jwu@mcvh-vcu.edu
Jian
J
Wu
Martin J
MJ
Murphy
Elisabeth
E
Weiss
William C
WC
Sleeman
Jeffrey
J
Williamson
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*