Tissue mixture-based inner bladder wall segmentation with applications in MRI-based virtual cystoscopy

Abstract

As a non-invasive bladder tumor screening approach, magnetic resonance imaging (MRI)-based virtual cystoscopy (VCys) has received increasing attention for a better soft tissue contrast compared to computer tomography (CT)-based VCys. In this paper, some preliminary work on segmenting the inner boundary of bladder wall from both T1- and T2- weighted MR bladder images were presented. Via an iterative maximum a posteriori expectation-maximization (MAPEM) approach, the tissue mixture fractions inside each voxel were estimated. Considering the partial volume effect (PVE) that MR images suffer from, the advantages of such mixture-based segmentation approach are (1) statistics-based tissue mixture model that shapes each tissue type as a normal-distributed random variable, (2) closed-form mathematical MAP-EM iterative solution, and (3) capability and efficiency of the estimated tissue mixture fractions in reflecting PVE. Given the extracted inner bladder wall, manipulations could be further taken, for each individual voxel located on the inner bladder wall, to identify the outer bladder wall prior to the measurement of wall thickness. Not limited to geometrical analysis, the consideration of PVE in the study of early stage abnormality on the mucosa in the scope of VCys is believed to provide more textural information in distinguishing from neighboring artifacts about the surface deformations that is due to bladder tumors.