A new partial volume segmentation approach to extract bladder wall for computer-aided detection in virtual cystoscopy

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. C 2016 American Association of Physicists in Medicine. [http://dx

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“…9 Other researchers have attempted to segment the bladder on various imaging modalities. Li et al 10 and Duan et al…”

Section: Introductionmentioning

confidence: 99%

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

et al. 2016

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“…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

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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“…Li et al (Li et al , 2004) segmented the bladder wall from magnetic resonance (MR) cytoscopy in 6 patients and analyzed it for suspected lesions using a partial volume segmentation algorithm. 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.…”

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.

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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.

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A new partial volume segmentation approach to extract bladder wall for computer-aided detection in virtual cystoscopy

Cited by 18 publications

References 18 publications

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

Urinary bladder segmentation in CT urography using deep‐learning convolutional neural network and level sets

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

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

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