Segmenting the prostate and rectum in CT imagery using anatomical constraints.
The automatic segmentation of the prostate and rectum from 3D computed tomography (CT) images is still a challenging problem, and is critical for image-guided therapy applications. We present a new, automatic segmentation algorithm based on deformable organ models built from previously segmented training data. The major contributions of this work are a new segmentation cost function based on a Bayesian framework that incorporates anatomical constraints from surrounding bones and a new appearance model that learns a nonparametric distribution of the intensity histograms inside and outside organ contours. We report segmentation results on 185 datasets of the prostate site, demonstrating improved performance over previous models.
Chen
Siqi
S
Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. chens@rpi.edu
Lovelock
D Michael
DM
Radke
Richard J
RJ
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008953:Models, Anatomic; D011467:Prostate / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated; D012007:Rectum / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*