0000000000399635
AUTHOR
J. Freixenet
Graph Cut Energy Minimization in a Probabilistic Learning Framework for 3D Prostate Segmentation in MRI
International audience; Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels is achieved with a probabilistic atlas and a random forest based learning framework. The posterior probabilities are combined to obtain the likelihood of a voxel being prostate. Finally, 3D graph cut based energy minimization in the stochastic space provides segmentation …
A Mumford-Shah functional based variational model with contour, shape, and probability prior information for prostate segmentation
Abstract: Inter patient shape, size and intensity variations of the prostate in transrectal ultrasound (TRUS) images challenge automatic segmentation of the prostate. In this paper we propose a variational model driven by Mumford-Shah (MS) functional for segmenting the prostate. Parametric representation of the implicit curve is derived from principal component analysis (PCA) of the signed distance representation of the labeled training data to impose shape prior. Posterior probability of the prostate region determined from random forest classification facilitates initialization and propagation of our model in a MS energy minimization framework. The proposed method achieves mean Dice simila…