NDLI: A unified EM approach to bladder wall segmentation with coupled level-set constraints

Content Provider	PubMed Central
Author	Han, Hao Li, Lihong Duan, Chaijie Zhang, Hao Zhao, Yang Liang, Zhengrong
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 T1-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 on to the model, where the bladder lesions will be eventually detected via experts’ visualization and/or computer-aided detection.
Related Links	http://dx.doi.org/10.1016/j.media.2013.08.002
Ending Page	1205
Page Count	14
Starting Page	1192
File Format	PDF
ISSN	13618415
e-ISSN	13618423
Journal	Medical image analysis
Issue Number	8
Volume Number	17
Language	English
Publisher Date	2013-12-01
Access Restriction	Open
Subject Keyword	Radiological and Ultrasound Technology Health Informatics Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Computer Graphics and Computer-Aided Design Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology

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