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

Duan, Chaijie; Zhang, Liang; Bao, Shanglian; Zhu, Huajian; Wang, Su; Zhang, Guangxiang; Chen, J.J.; Lu, Hongbing

doi:10.1109/tmi.2009.2039756

Cited by 72 publications

(40 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Duan et al (Duan et al , 2010) used two collaborative level set functions and clustering to segment the bladder, also in MR cytoscopy of 6 patients. In a different study, Duan et al (Duan et al , 2012) developed a segmentation method using MR images of 10 patients, which used an adaptive window-setting scheme to detect tumor surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al (Li et al , 2004) did not report quantitative results for the bladder segmentation. Duan et al (Duan et al , 2010, Duan et al , 2012) and Han et al (Han et al , 2013) evaluated the segmentation performances using radiologists’ subjective ratings without reporting quantitative results. Chai et al (Chai et al , 2012) presented a method for semiautomatic bladder segmentation on cone beam CT by using a population-based statistical bladder shape calculated using spherical harmonics description, then applying principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CT urography: segmentation of urinary bladder using CLASS with local contour refinement

Cha

Hadjiiski²,

Chan³

et al. 2014

Phys. Med. Biol.

View full text Add to dashboard Cite

Purpose We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. Methods The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. Previously, we proposed a Conjoint Level set Analysis and Segmentation System (CLASS). In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast-filled (C) region separately and automatically conjoins the NC and C region contours; however, inaccuracies in the NC and C region contours may cause the conjoint contour to exclude portions of the bladder. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). MGR propagates the C region contours if the level set propagation in the C region stops prematurely due to substantial non-uniformity of the contrast. EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. EDWP uses changes in energies, smoothness criteria of the contour, and previous slice contour to determine when to stop the propagation, following decision rules derived from training. A data set of 173 cases was collected for this study: 81 cases in the training set (42 lesions, 21 wall thickenings, 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, 13 normal bladders). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy. Results For CLASS with LCR, the average volume intersection ratio, average volume error, absolute average volume error, average minimum distance and Jaccard index were 84.2±11.4%, 8.2±17.4%, 13.0±14.1%, 3.5±1.9 mm, 78.8±11.6%, respectively, for the training set and 78.0±14.7%, 16.4±16.9%, 18.2±15.0%, 3.8±2.3 mm, 73.8±13.4% respectively, for the test set. With CLASS only, the corresponding values were 75.1±13.2%, 18.7±19.5%, 22.5±14.9%, 4.3±2.2 mm, 71.0±12.6%, respectively, for the training set and 67.3±14.3%, 29.3±15.9%, 29.4±15.6%, 4.9±2.6 mm, 65.0±13.3%, respectively, for the test set. The differences between the two methods for all five measures were statistically significant (p<0.001) for both the training and test sets. Conclusions The results demonstrate the potential of CLASS with LCR for segmentation of the bladder.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CT urography: segmentation of urinary bladder using CLASS with local contour refinement

Cha

Hadjiiski²,

Chan³

et al. 2014

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…Our previous work on MR-based VCys (Li et al ., 2008, 2004, 2003b) has shown promising results for the segmentation of the inner bladder wall, where a mixture-based segmentation algorithm with Markov random field (MRF) model-based prior is employed to segment multispectral MR images. Nevertheless, the segmentation of the outer bladder wall was not resolved until the introduction of a coupled level-set (CLS) approach on T 1 -weighted MR images (Duan et al ., 2010). Recently, Chi et al (2011) applied a geodesic active contour (GAC) model in T 2 -weighted image to segment the inner bladder wall, and then coupled the constraint of maximum wall thickness in T 1 -weighted image to segment the outer wall.…”

Section: Introductionmentioning

confidence: 99%

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

Han

Duan

et al. 2013

Medical Image Analysis

View full text Add to dashboard 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.

show abstract

“…5,[18][19][20][21][22][23][24][25][26][27] Existing active contours models can be classified into two categories: edge-based models [18][19][20][21] and region-based models. [22][23][24][25][26][27] The geodesic active contour model 21 (GAC) is a typical edgebased model. It utilizes image gradient information to guide evolving curve, and can detect a specific target from a complex background.…”

Section: Introductionmentioning

confidence: 99%

An active contour model for medical image segmentation with application to brain CT image

et al. 2013

View full text Add to dashboard Cite

Purpose: Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images. Methods:The energy function of the region-based active contour model is composed of a range domain kernel function, a space domain kernel function, and an edge indicator function. By minimizing the energy function, the region of edge elements of the target could be automatically identified in images with less dependence on initial contours. The energy function was optimized by means of the deepest descent method with a level set framework. An overlap rate between segmentation results and the reference standard was used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on both synthetic data and real brain CT images. They also compared the performance level of our method to those of region-scalable fitting (RSF) and global convex segment (GCS) models. Results: For the experiment of CSF segmentation in 67 brain CT images, their method achieved an average overlap rate of 66% compared to the average overlap rates of 16% and 46% from the RSF model and the GCS model, respectively. Conclusions: Their region-based active contour model has the ability to achieve accurate segmentation results in images with high noise level and intensity inhomogeneity. Therefore, their method has great potential in the segmentation of medical images and would be useful for developing CAD schemes for acute ischemic stroke in brain CT images.

show abstract

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

Cited by 72 publications

References 30 publications

CT urography: segmentation of urinary bladder using CLASS with local contour refinement

CT urography: segmentation of urinary bladder using CLASS with local contour refinement

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

An active contour model for medical image segmentation with application to brain CT image

Contact Info

Product

Resources

About