![]() 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. We are developing a CAD system that detects bladder cancer in CTU, and a critical part of this system is accurate bladder segmentation that isolates the bladder from the surrounding anatomical structures. Computer-aided detection (CAD) used as an adjunct may be the tool that reduces the chance of oversight by the radiologists. Due to the workload of interpreting CTU studies, the chance for the radiologists’ to miss a subtle lesion may not be negligible, thus any technique that may help radiologists with identification of urothelial neoplasms within the urinary tract will be useful. The challenges of analyzing a CTU study leads to a substantial variability among radiologists in detection of bladder cancer, with reported sensitivities ranging from 59% to 92% ( Park et al., 2007 Sudakoff et al., 2008). Not only do the radiologists have to identify these anomalies, they must also determine how likely each of them is an urothelial neoplasm. In addition, many different urinary anomalies may be found in a single CTU study. The possibility that multiple lesions may be present requires that the radiologists pay close attention throughout the entire urinary tract. The radiologists interpreting the study have to visually determine whether or not lesions are present within the urinary tracts, frequently needing to adjust the brightness and contrast of the images and use zooming from a display workstation. On average, 300 slices are generated for each CTU scan at a slice interval of either 1.25 mm or 0.625 mm (range: 200 to 600 slices). Interpretation of a CTU study requires thorough image analysis, often requiring extensive time. CT urography (CTU), therefore, may spare the patients from having to undergo other imaging studies (intravenous pyelogram (IVP), ultrasound, conventional abdominal CT, and even MRI), thereby reducing health care costs ( Akbar et al., 2004 Caoili et al., 2002 Liu et al., 2005 McCarthy and Cowan, 2002 Noroozian et al., 2004). Multi-detector row CT (MDCT) urography has shown promise of detecting bladder lesions and has become the imaging modality of choice for most urinary track abnormalities since a single exam can be used to evaluate the kidneys, intrarenal collecting systems, and ureters. If the cancer is detected after it has invaded the bladder wall but is still confined to the bladder, the 5-year survival rate drops to 63% however, only about 50% of the patients are diagnosed before the cancer has invaded the muscular bladder wall ( American Cancer Society, 2013). If bladder cancers are detected and treated while the cancer is confined within the bladder’s inner lining but has not invaded the muscular bladder wall, the 5-year survival rate is 88%. Early detection and treatment of bladder cancer increases patient survivability. The American Cancer Society estimates that bladder cancer will cause 15,210 deaths (10,820 in men, 4,390 in women) in the United States in 2013, with 72,570 new cases (54,610 in men, 17,960 in women) diagnosed ( American Cancer Society, 2013). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy.īladder cancer is the fourth most common cancer diagnosed in men. 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). ![]() 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. ![]() EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. 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. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). 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. Previously, we proposed a Conjoint Level set Analysis and Segmentation System (CLASS). The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. ![]()
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