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

Cha, Kenny; Hadjiiski, Lubomir M.; Chan, Heang Ping; Caoili, Elaine M.; Cohan, Richard H.; Zhou, Chuan

doi:10.1088/0031-9155/59/11/2767

Cited by 15 publications

(14 citation statements)

References 17 publications

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“…The performance metrics are described briefly below and more details can be found in our previous studies. 17,18 The volume intersection ratio (R 3D ) is the ratio of the intersection between the reference volume and the given volume to the reference volume,…”

Section: E Evaluation Methodsmentioning

confidence: 99%

“…Training set 84.3 ± 7.1 9.7 ± 10.0 11.4 ± 7.9 3.4 ± require two manually input VOIs: one for the noncontrast region and the other for the contrast-enhanced region using our CLASS segmentation method 17,18 and an LCR method was needed to refine and connect the two contours. However, by combining the DL-CNN bladder likelihood maps with the level set methods, we no longer needed the separate input user inputs for the two different regions.…”

Section: Dl-cnn Vs Rs2mentioning

confidence: 99%

“…They qualitatively evaluated the segmentation performance of 81 bladders and performed quantitative evaluation of 30 bladders comparing the computer-segmented contours to hand-segmented reference contours and obtained promising results. The CLASS method was further developed by Cha et al 18 to improve the segmentation accuracy. Modelguided refinement was used to propagate the contours of the contrast-enhanced region if the level set propagation stopped prematurely due to substantial nonuniformity of the contrast.…”

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confidence: 99%

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

et al. 2016

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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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Section: E Evaluation Methodsmentioning

confidence: 99%

Section: Dl-cnn Vs Rs2mentioning

confidence: 99%

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confidence: 99%

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

et al. 2016

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“…We introduced CLASS previously. 12,13 CLASS consists of four stages: (1) preprocessing and initial segmentation, (2) 3D level set segmentation, (3) 2D level set segmentation, and (4) local contour refinement. CLASS segments the noncontrast and the contrast-enhanced regions of the bladder by applying the level sets to each region separately, refining the contours, and then automatically conjoins them.…”

Section: B Bladder Segmentation Using Conjoint Level Set Analysis mentioning

confidence: 99%

“…The boxes were generated by a single researcher who was trained by the radiologists and has been involved in the development of the system for 2 yr. More details of the bladder segmentation methods can be found in the literature. 12,13 An example of a bladder and its segmentation are shown in Figs. 4(a) and 4(b), respectively.…”

Section: B Bladder Segmentation Using Conjoint Level Set Analysis mentioning

confidence: 99%

Detection of urinary bladder mass in CT urography with SPAN

et al. 2015

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Purpose: The authors are developing a computer-aided detection system for bladder cancer on CT urography (CTU). In this study, the authors focused on developing a system for detecting masses fully or partially within the contrast-enhanced (C) region of the bladder. Methods: With IRB approval, a data set of 70 patients with biopsy-proven bladder lesions fully or partially immersed within the contrast-enhanced region (C region) of the bladder was collected for this study: 35 patients for the training set (39 malignant, 7 benign lesions) and 35 patients for the test set (49 malignant, 4 benign lesions). The bladder in the CTU images was automatically segmented using the authors' conjoint level set analysis and segmentation system, which they developed specifically to segment the bladder. A closed contour of the C region of the bladder was generated by maximum intensity projection using the property that the dependently layering contrast material in the bladder will be filled consistently to the same level along all CTU slices due to gravity. Potential lesion candidates within the C region contour were found using the authors' Straightened Periphery ANalysis (SPAN) method. SPAN transforms a bladder wall to a straightened thickness profile, marks suspicious pixels on the profile, and clusters them into regions of interest to identify potential lesion candidates. The candidate regions were automatically segmented using the authors' autoinitialized cascaded level set segmentation method. Twenty-three morphological features were automatically extracted from the segmented lesions. The training set was used to determine the best subset of these features using simplex optimization with the leave-one-out case method. A linear discriminant classifier was designed for the classification of bladder lesions and false positives. The detection performance was evaluated on the independent test set by free-response receiver operating characteristic analysis. Results: At the prescreening step, the authors' system achieved 84.4% sensitivity with an average of 4.3 false positives per case (FPs/case) for the training set, and 84.9% sensitivity with 5.4 FPs/case for the test set. After linear discriminant analysis (LDA) classification with the selected features, the FP rate improved to 2.5 FPs/case for the training set, and 4.3 FPs/case for the test set without missing additional true lesions. By varying the threshold for the LDA scores, at 2.5 FPs/case, the sensitivities were 84.4% and 81.1% for the training and test sets, respectively. At 1.7 FPs/case, the sensitivities decreased to 77.8% and 75.5%, respectively. Conclusions:The results demonstrate the feasibility of the authors' method for detection of bladder lesions fully or partially immersed in the contrast-enhanced region of CTU. C 2015 American Association of Physicists in Medicine. [http://dx

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Deep‐learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography

et al. 2019

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Purpose We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urography (CTU). Materials and Methods A data set of 172 CTU cases was collected retrospectively with Institutional Review Board (IRB) approval. The data set was randomly split into two independent sets of training (81 cases) and testing (92 cases) which were manually outlined for both the inner and outer wall. We trained a deep‐learning convolutional neural network (DL‐CNN) to distinguish the bladder wall from the inside and outside of the bladder using neighborhood information. Approximately, 240 000 regions of interest (ROIs) of 16 × 16 pixels in size were extracted from regions in the training cases identified by the manually outlined inner and outer bladder walls to form a training set for the DL‐CNN; half of the ROIs were selected to include the bladder wall and the other half were selected to exclude the bladder wall with some of these ROIs being inside the bladder and the rest outside the bladder entirely. The DL‐CNN trained on these ROIs was applied to the cases in the test set slice‐by‐slice to generate a bladder wall likelihood map where the gray level of a given pixel represents the likelihood that a given pixel would belong to the bladder wall. We then used the DL‐CNN likelihood map as an energy term in the energy equation of a cascaded level sets method to segment the inner and outer bladder wall. The DL‐CNN segmentation with level sets was compared to the three‐dimensional (3D) hand‐segmented contours as a reference standard. Results For the inner wall contour, the training set achieved the average volume intersection, average volume error, average absolute volume error, and average distance of 90.0 ± 8.7%, −4.2 ± 18.4%, 12.9 ± 13.9%, and 3.0 ± 1.6 mm, respectively. The corresponding values for the test set were 86.9 ± 9.6%, −8.3 ± 37.7%, 18.4 ± 33.8%, and 3.4 ± 1.8 mm, respectively. For the outer wall contour, the training set achieved the values of 93.7 ± 3.9%, −7.8 ± 11.4%, 10.3 ± 9.3%, and 3.0 ± 1.2 mm, respectively. The corresponding values for the test set were 87.5 ± 9.9%, −1.2 ± 20.8%, 11.9 ± 17.0%, and 3.5 ± 2.3 mm, respectively. Conclusions Our study demonstrates that DL‐CNN‐assisted level sets can effectively segment bladder walls from the inner bladder and outer structures despite a lack of consistent distinctions along the inner wall. However, even with the addition of level sets, the inner and outer walls may still be over‐segmented and the DL‐CNN‐assisted level sets may incorrectly segment parts of the prostate that overlap with the outer bladder wall. The outer wall segmentation was improved compared to our previous method and the DL‐CNN‐assisted level sets were also able to segment the inner bladder wall with similar performance. This study shows the DL‐CNN‐assisted level set segmentation tool can effectively segment the inner and outer wall of the bladder.

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

Cited by 15 publications

References 17 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

Detection of urinary bladder mass in CT urography with SPAN

Deep‐learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography

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