title: A unified EM approach to bladder wall segmentation with coupled level-set constraints.
abstract: Magnetic resonance (MR) imaging-based virtual cystoscopy (VCys), as a non-invasive, safe and cost-effective technique, has shown its promising virtue for early diagnosis and recurrence management of bladder carcinoma. One primary goal of VCys is to identify bladder lesions with abnormal bladder wall thickness, and consequently a precise segmentation of the inner and outer borders of the wall is required. In this paper, we propose a unified expectation-maximization (EM) approach to the maximum-a posteriori (MAP) solution of bladder wall segmentation, by integrating a novel adaptive Markov random field (AMRF) model and the coupled level-set (CLS) information into the prior term. The proposed approach is applied to the segmentation of T(1)-weighted MR images, where the wall is enhanced while the urine and surrounding soft tissues are suppressed. By introducing scale-adaptive neighborhoods as well as adaptive weights into the conventional MRF model, the AMRF model takes into account the local information more accurately. In order to mitigate the influence of image artifacts adjacent to the bladder wall and to preserve the continuity of the wall surface, we apply geometrical constraints on the wall using our previously developed CLS method. This paper not only evaluates the robustness of the presented approach against the known ground truth of simulated digital phantoms, but further compares its performance with our previous CLS approach via both volunteer and patient studies. Statistical analysis on experts' scores of the segmented borders from both approaches demonstrates that our new scheme is more effective in extracting the bladder wall. Based on the wall thickness calibrated from the segmented single-layer borders, a three-dimensional virtual bladder model can be constructed and the wall thickness can be mapped onto the model, where the bladder lesions will be eventually detected via experts' visualization and/or computer-aided detection.
authors.affiliation: Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Han
authors.forename: Lihong
authors.initials: L
authors.lastname: Li
authors.forename: Chaijie
authors.initials: C
authors.lastname: Duan
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D001743:Urinary Bladder / Q000473:pathology*; D001745:Urinary Bladder Diseases / Q000473:pathology*
keywords: Bladder wall segmentation; Coupled level-set; EM algorithm; MAP solution; Virtual cystoscopy