Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape.
Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation
therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and
context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful
patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this
paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is
segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by
a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach
is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The
refinement model is based on a graph-search based framework, which contains both shape and topology information
during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and
region-specific classifier training. The proposed algorithm was developed using 261 training images and tested
on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on
the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40
seconds depending on image size and resolution.