Automated Delineation of the Normal Urinary Bladder on Planning CT and Cone Beam CT

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• 作者：Tara Rosewall ; PhD ; FCAMRT^a ; ^b ; ^{Tara.Rosewall@rmp.uhn.on.ca" class="auth_mail" title="E-mail the corresponding author} ; Jason Xie ; MSc^a ; Vickie Kong ; BSc ; MRT(T)^a ; Andrew J. Bayley ; MD^a ; Peter Chung ; MD^a ; Geoffrey Currie ; PhD^b ; ^c ; Janelle Wheat ; DHlthSc^b ; ^c ; Michael Milosevic ; MD^a
• 关键词：Prostate cancer ; radiation therapy ; contouring ; organs at risk ; model-based segmentation ; contour propagation
• 刊名：Journal of Medical Imaging and Radiation Sciences
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：47
• 期：1
• 页码：21-29
• 全文大小：1305 K

文摘

To quantify the volumetric effect of delineation variability when using manual versus semiautomated tools to contour the normal bladder on planning computed tomography (CT) and cone beam CT.

Methods

Following research ethics board approval, 10 prostate cancer patients were selected. For each patient, one pretreatment cone beam CT (CBCT) was randomly selected from the first treatment week and registered to the planning CT (planCT). Model-based auto adaptation was used to delineate the outer bladder (OB) surface for the planCT. That contour was then propagated and manually adapted onto the CBCT. A second observer delineated OB for the planCT and CBCT using typical manual methods. These delineation procedures were repeated four times on each image set, with observers blinded to the previous contours. Metrics of volumetric, geometric, and overlap concordance were used to compare the manual and automated OB contours.

Results

The mean pairwise difference between the manual and model-based planCT volumes was 4 cm³ (2%), and the model-based contours exhibited approximately half the observer variation of the manual ones (3 cm³, 2%). The mean of pairwise differences between the manual and propagated CBCT volumes was 13 cm³ (8%), but the propagated contours exhibited approximately half the observer related volume variation (11 cm³, 6%). Small CBCT bladder volumes displayed larger observer variation with manual methods (r², −0.640). Variability between the automated contours was significantly smaller than for the corresponding manual observations (P = .004 and .002, respectively). Metrics of three-dimensional overlap concordance indicated excellent agreement within and between the delineation methods. Automated CBCT contours were significantly smoother than the manual ones (surface sphericity index, 1.29 vs. 1.35; P = .03).

Conclusions

Volumetric, geometric, and overlap metrics all indicated that planCT and CBCT automated OB contours fell within the range of manually delineated contours. The CBCT propagated contours were significantly smoother and associated with smaller intraobserver variability, compared with manual contours. Importantly, the findings from this research suggest that contour propagation may be more robust than manual delineation, especially in the presence of poor image quality.