Tumor Detection by Virtual Cystoscopy With Color Mapping of Bladder Wall Thickness

Hoyte, Lennox X; Okon, S; Schreyer, A.; Lee, Jhelmon; Zou, Kelly H.; Warfield, Simon K.; Richie, Jerome P.; Loughlin, Kevin R.; O’Leary, Michael P.; Doyle, C. J.; Kikinis, Ron

doi:10.1097/00005392-200202000-00024

Cited by 32 publications

(19 citation statements)

References 14 publications

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“…The fundamental conjecture of this study and the previous work is that the wall thickness is a sensitive biomarker to detect bladder abnormalities, as observed by several studies [4, 5]. It is expected that both geometrical and texture features derived from the extracted wall would further improve the sensitivity of the biomarker.…”

Section: Discussionsupporting

confidence: 54%

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

Duan

Yuan

Liu

et al. 2011

IEEE Trans. Biomed. Eng.

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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.

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Section: Discussionsupporting

confidence: 54%

“…Conventional characteristic features, like curvedness and shape index, vary significantly from voxel to voxel [3], causing high false positive (FP) rate. Preliminary results show that the bladder wall thickness can be a biomarker for detection of the abnormalities [4~6]. The method in [5] generated promising results.…”

Section: Introductionmentioning

confidence: 99%

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

Duan

Yuan

Liu

et al. 2011

IEEE Trans. Biomed. Eng.

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“…Among many seemingly viable features, the bladder wall thickness (BWT) has been clinically recognized as a persuasive indicator or biomarker for bladder abnormality [13], because locally-thickened wall often appears around pathological abnormalities. Therefore, BWT is often regarded as a determinative feature for detection of abnormalities by human experts [14, 15, 16] and/or CAD schemes [17]. In addition to the local thickening feature, the global picture or the pattern of the thickness distribution on the entire wall can be another feature for early sign of abnormalities [18].…”

Section: Introductionmentioning

confidence: 99%

“…In some clinical cases, a sharp inward bump on the outer border could cause the tip of the bump to be the closest point to many of the points on the inner border. Their subsequent work [14] adopted exactly the same strategy as in [21]. In a later work [15], a similar strategy was adapted onto the triangle mesh, where, instead of defining the distance on voxels, they measured the distance on the generated triangle mesh.…”

Section: Introductionmentioning

confidence: 99%

Bladder wall thickness mapping for magnetic resonance cystography

et al. 2013

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Clinical studies have shown the evidence that the bladder wall thickness is an effective biomarker for bladder abnormalities. The clinical optical cystoscopy, the current gold standard, cannot show the wall thickness. The use of ultrasound by experts may generate some local thickness information, but the information is limited in field-of-view and is user dependent. Recent advances in magnetic resonance (MR) imaging technologies lead MR-based virtual cystoscopy or MR cystography toward a potential alternative to map the wall thickness for the entire bladder. From a high resolution structural MR volumetric image of the abdomen, a reasonable segmentation of the inner and outer borders of the bladder wall can be achievable. Starting from here, this paper reviews the limitation of a previous distance field-based approach of measuring the thickness between the two borders and then provides a solution to overcome the limitation by an electric field-based strategy. In addition, this paper further investigates a surface fitting strategy to minimize the discretization errors on the voxel-like borders and facilitate the thickness mapping on the three-dimensional patient-specific bladder model. The presented thickness calculation and mapping were tested on both phantom and human subject datasets. The results are preliminary but very promising with a noticeable improvement over the previous distance field-based approach.

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“…However, the sensitivity of CT to soft tissues (including the urine) prohibits itself from providing good image contrast in bladder wall [10]. This limitation can be partially mitigated by injection of contrast medium [8, 9, 11, 12]. Unfortunately, not only this procedure becomes invasive and uncomfortable, the CT scanning also delivers excessive X-ray exposure to the patients, both of which considerably increase the patients’ risk.…”

Section: Introductionmentioning

confidence: 99%

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

Duan

Zhang

Bao

et al. 2010

IEEE Trans. Med. Imaging

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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.

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Tumor Detection by Virtual Cystoscopy With Color Mapping of Bladder Wall Thickness

Cited by 32 publications

References 14 publications

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

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

Bladder wall thickness mapping for magnetic resonance cystography

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

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