Segmentation of multispectral bladder MR images with inhomogeneity correction for virtual cystoscopy

et al. 2013

Medical Image Analysis

Self Cite

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.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Han

et al. 2013

Medical Image Analysis

Self Cite

“…This segmentation algorithm provides the wall borders, which may help the following geometry analysis as comparing to our previous segmentation of partial volume layers each with a thickness due to the partial volume effect [3, 15, 16]. …”

Section: Bladder Wall Layer and Path Generationsmentioning

confidence: 99%

Volume-Based Features for Detection of Bladder Wall Abnormal Regions via MR Cystography

IEEE Trans. Biomed. Eng.

Yuan

Liu

et al. 2011

This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall thickness and a bent rate term to measure the geometry property of the voxels on the inner border, the seed voxels representing the abnormalities on the inner border are determined. Then, by tracing the path from each seed, a weighted bent rate term is constructed to determine the suspected voxels, which are on the path and inside the bladder wall. All the suspected voxels are grouped together for the abnormal region. This work is significantly different from most of the previous computer aided bladder tumor detection reports on two aspects. First of all, the T1-weighted MR images are used which give better image contrast and texture information for the bladder wall, comparing with the computed tomography images. Secondly, while most previous reports detected the abnormalities and indicated them on the reconstructed three-dimensional bladder model by surface rendering, we further determine the possible region of the abnormality inside the bladder wall. This work aims at a non-invasive procedure for bladder tumor detection and abnormal region delineation, which has the potential for further clinical analysis such as the invasion depth of the tumor and virtual cystoscopy diagnosis. Five datasets including two patients and three volunteers were used to test the presented method, all the tumors were detected by the method, and the overlap rates of the regions delineated by the computer against the experts were measured. The results demonstrated the potential of the method for detecting bladder wall abnormal regions via MR cystography.

“…In contrast, for a small bump protruding out of the bladder wall, the measurement of its thickness as the distance between the inner and outer borders tends to be a good indicator of the occurrence of abnormalities [8, 11~14]. Towards that end, the issue of accurately delineating the bladder wall inner and outer borders arises [18~20]. …”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose a novel segmentation approach, alternative to our previous segmentation work [18~20], to delineate the inner and outer borders. From the segmented borders, wall thickness is measured by the length of the integral path which mimics the electric field line between two iso-potential surfaces.…”

Section: Introductionmentioning

confidence: 99%

A Coupled Level Set Framework for Bladder Wall Segmentation With Application to MR Cystography

IEEE Trans. Med. Imaging

Zhang

Bao

et al. 2010

In this paper, we propose a coupled level set (LS) framework for segmentation of bladder wall using T1-weighted magnetic resonance (MR) images with clinical applications to virtual cystoscopy (i.e., MR cystography). The framework uses two collaborative LS functions and a regional adaptive clustering algorithm to delineate the bladder wall for the wall thickness measurement on a voxel-by-voxel basis. It is significantly different from most of the pre-existing bladder segmentation work in four aspects. First of all, while most previous work only segments the inner border of the wall or at most manually segments the outer border, our framework extracts both the inner and outer borders automatically except that the initial seed point is given by manual selection. Secondly, it is adaptive to T1-weighted images with decreased intensities in urine, as opposed to enhanced intensities in T2-weighted scenario and computed tomography. Thirdly, by considering the image global intensity distribution and local intensity contrast, the defined image energy function in the framework is more immune to inhomogeneity effect, motion artifacts and image noise. Finally, the bladder wall thickness is measured by the length of integral path between the two borders which mimic the electric field line between two iso-potential surfaces. The framework was tested on six datasets with comparison to the well-known Chan-Vese (C-V) LS model. Five experts blindly scored the segmented inner and outer borders of the presented framework and the C-V model. The scores demonstrated statistically the improvement in detecting the inner and outer borders.