title: CT urography: segmentation of urinary bladder using CLASS with local contour refinement.
abstract: We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. 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. 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. The results demonstrate the potential of CLASS with LCR for segmentation of the bladder.
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, MI 48109-0904, USA.
authors.forename: Kenny
authors.initials: K
authors.lastname: Cha
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Caoili
authors.forename: Richard H
authors.initials: RH
authors.lastname: Cohan
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D001743:Urinary Bladder / Q000000981:diagnostic imaging*; D014567:Urography / Q000379:methods*