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The aim of this study is to develop a novel semiautomatic bladder segmentation approach for selecting the appropriate plan from the library of plans for a multiple-plan adaptive radiotherapy (ART) procedure. A population-based statistical bladder model was first built from a training data set (95 bladder contours from 8 patients). This model was then used as constraint to segment the bladder in an independent validation data set (233 CBCT scans from the remaining 22 patients). All 3D bladder contours were converted into parametric surface representations using spherical harmonic expansion. Principal component analysis (PCA) was applied in the spherical harmonicbased shape parameter space to calculate the major variation of bladder shapes. The number of dominating PCA modes was chosen such that 95% of the total shape variation of the training data set was described. The automatic segmentation started from the bladder contour of the planning CT of each patient, which was modified by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to best fit the bladder boundary in the localization CBCT image. A cost function was defined to measure the goodness of fit of the segmentation on the localization CBCT image. The segmentation was obtained by minimizing this cost function using a simplex optimizer. After automatic segmentation, a fast manual correction method was provided to correct those bladders (parts) that were poorly segmented. Volume- and distance-based metrics and the accuracy of plan selection from multiple plans were evaluated to quantify the performance of the automatic and semiautomatic segmentation methods. For the training data set, only seven PCA modes were needed to represent 95% of the bladder shape variation. The mean CI overlap and residual error (SD) of automatic bladder segmentation over all of the validation data were 70.5% and 0.39 cm, respectively. The agreement of plan selection between automatic bladder segmentation and manual delineation was 56.7%. The automatic segmentation and visual assessment took on average 7.8 and 9.7 s, respectively. In 53.4% of the cases, manual correction was performed after automatic segmentation. Themanual correction improved the mean CI overlap, mean residual error and plan selection agreement to 77.7%, 0.30 cmand 80.7%, respectively. Manual correction required on average 8.4 markers and took on average 35.5 s. The statistical shape-based segmentation approach allows automatic segmentation of the bladder on CBCT with moderate accuracy. Limited user intervention can quickly and reliably improve the bladder contours. This segmentation method is suitable to select the appropriate plan for multiple-plan ART of bladder cancer. [ABSTRACT FROM AUTHOR]

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patientspecific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCAmodes with less than 0.1 cmmean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation CBCT sampled on the segmentation contour candidate. The cost functionmeasured the goodness of fit of the segmentation on the validation image andwasminimized using a simplex optimizer. For each validation CBCT image, the segmentations were done five times using a different reference CBCT. The one with the lowest cost function was selected as the final bladder segmentation. Volume- and distance-based metrics and the accuracy of plan selection were evaluated to quantify the performance. Two to four PCA modes were needed to represent the bladder shape variation with less than 0.1 cm average residual error for the training data of each patient. The automatically segmented bladders had a 78.5% mean conformity index with the manual delineations. The mean SD of the local residual error over all patients was 0.24 cm. The agreement of plan selection between automatic and manual bladder segmentations was 77.5%. PCA is an efficient method to describe patient-specific bladder deformation. The statistical-shape-based segmentation approach is robust to handle the relatively poor CBCT image quality and allows for fast and reliable automatic segmentation of the bladder on CBCT for selecting the appropriate plan from a library of plans. [ABSTRACT FROM AUTHOR]

Purpose: Bladder tumor delineation and localization during treatment are challenging problems in radiotherapy for bladder cancer. The purpose of this study is to investigate improvement of tumor delineation by the fusion of cystoscopy images with the planning CT-scan using lipiodol markers injected around the visible tumor during cystoscopy., Methods: A registration method was developed for the fusion of cystoscopy images with a planning CT-scan and was tested on a phantom and retrospectively on the imaging data of four bladder cancer patients. For the patients, small deposits of lipiodol were injected at the visible margin of the tumor or previous transurethral resection site during cystoscopy. These deposits were clearly visible on the planning CT-scan and served as markers for both tumor delineation and image guidance of the radiotherapy treatment. Here, the markers were used for the registration of cystoscopy images with the planning CT-scan. The registration procedure works as follows: First, coarse registrations were made to orient the cystoscopy image correctly, using the center of gravity of the markers, the center of the CT bladder, and one of N markers as fiducial points in a point matching procedure. Starting from these N orientations, full registrations are performed taking lens deformation into account. Since a cystoscopy image is 2D, each pixel corresponds to a line-of-sight. The distances between the CT markers and the lines-of-sight of the cystoscopy markers were minimized. The final cost function (the root mean square distance between corresponding CT markers and lines-of-sight) was used to quantify the quality of the registration. The registration with the lowest final cost was considered to represent the correct orientation. The CT-based tumor delineation was finally backprojected onto the cystoscopy image., Results: The fusion of cystoscopy images with a planning CT-scan succeeded for the phantom and three out of four patients. The fiducial registration error (FRE) for the phantom image registration based on five markers was 1.1 mm, while the target registration error was 1.2-1.7 mm. The FREs for the patient images were 0.1-3.6 mm. The registration procedure failed for one patient, since it was not possible to indicate unambiguously the corresponding lipiodol marker locations in the cystoscopy image and the planning CT-scan. The difference between the CT and cystoscopy defined tumor outlines clearly exceeded the registration accuracy., Conclusions: Registration of cystoscopy images and planning CT-scan is feasible and allows for improvement of tumor delineation. However, the lipiodol injection protocol needs to be improved to facilitate identification of markers on both cystoscopy images and planning CT-scans.