Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model.
OBJECTIVE
The authors present a fully automatic algorithm for the segmentation of the prostate in three-dimensional magnetic resonance (MR) images.


METHODS
The approach requires the use of an anatomical atlas which is built by computing transformation fields mapping a set of manually segmented images to a common reference. These transformation fields are then applied to the manually segmented structures of the training set in order to get a probabilistic map on the atlas. The segmentation is then realized through a two stage procedure. In the first stage, the processed image is registered to the probabilistic atlas. Subsequently, a probabilistic segmentation is obtained by mapping the probabilistic map of the atlas to the patient's anatomy. In the second stage, a deformable surface evolves toward the prostate boundaries by merging information coming from the probabilistic segmentation, an image feature model and a statistical shape model. During the evolution of the surface, the probabilistic segmentation allows the introduction of a spatial constraint that prevents the deformable surface from leaking in an unlikely configuration.


RESULTS
The proposed method is evaluated on 36 exams that were manually segmented by a single expert. A median Dice similarity coefficient of 0.86 and an average surface error of 2.41 mm are achieved.


CONCLUSIONS
By merging prior knowledge, the presented method achieves a robust and completely automatic segmentation of the prostate in MR images. Results show that the use of a spatial constraint is useful to increase the robustness of the deformable model comparatively to a deformable surface that is only driven by an image appearance model.
Martin
Sébastien
S
TIMC IMAG Laboratory, University Joseph Fourier, CNRS UMR5525, Grenoble, 38710 La Tronche, France. sebastien.martin@imag.fr
Troccaz
Jocelyne
J
Daanenc
Vincent
V
D000465:Algorithms; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008495:Medical Oncology / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011336:Probability; D011467:Prostate / Q000000981:diagnostic imaging / Q000473:pathology; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000628:therapy*; D011859:Radiography; D012984:Software; D013499:Surface Properties