A Coarse-to-fine Shape Prior for Probabilistic Segmentations Using A Deformable M-rep

Training a shape prior has been potent scheme for anatomical object segmentations, especially for images with noisy or weak intensity patterns. When the shape representation lives in a high dimensional space, Principal Component Analysis (PCA) is often used to calculate a low dimensional variation subspace from frequently limited number of training samples. However, the eigenmodes of the subspace tend to keep the coarse variation of the shape only, losing the detailed localized variability which is crucial to accurate segmentations. In this paper, we propose a coarse-to-fine shape prior for probabilistic segmentation to enable local refinement, using a deformable medial representation, called the m-rep. Tests on the goodness of the shape prior are carried out on large simulated data sets of a) 1000 deformed ellipsoids with mixed global deformations and local perturbation; b) 500 simulated hippocampus models. The predictability of the shape priors are evaluated and compared by a squared correlations metric and the volume overlap measurement against different training sample sizes. The improved robustness achieved by the coarse-to-fine strategy is demonstrated, especially for low sample size applications. Finally, posterior segmentations of bladder in 3D CT images from multiple patients in day-to-day adaptive radiation therapy validate the local residual statistics that are introduced by this method satisfactorily improves the segmentation accuracy.