Hadjiiski Lubomir, Chan Heang-Ping, Caoili Elaine M, Cohan Richard H
University of Michigan, Department of Radiology, Ann Arbor, MI 48109, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3978-81. doi: 10.1109/EMBC.2012.6346838.
We are developing a Conjoint Level set Analysis and Segmentation System (CLASS) for bladder segmentation on CTU, which is a critical component for computer aided diagnosis of bladder cancer. A challenge for bladder segmentation is the presence of regions without contrast (NC) and filled with IV contrast (C). According to the characteristics of the bladder in CTU, CLASS is designed to perform number tasks such as preprocessing, initial segmentation, 3D and 2D level set segmentation and post-processing. CLASS segments separately the NC and the C regions of the bladder. In the post-processing stage the final contour is obtained based on the union of the NC and C contours. 70 bladders were segmented. Of the 70 bladders 31 contained lesions, 24 contained wall thickening, and 15 were normal. The performance of CLASS was assessed by rating the quality of the contours on a 5-point scale (1="very poor", 3="fair", 5="excellent"). The average quality ratings for the 12 completely no contrast (NC) and 5 completely contrast-filled (C) bladder contours were 3.3±1.0 and 3.4±0.5, respectively. The average quality ratings for the 53 NC and 53 C regions of the 53 partially contrast-filled bladders were 4.0±0.7 and 4.0±1.0, respectively. Quality ratings of 4 or above were given for 87% (46/53) NC and 77% (41/53) C regions. Only 4% (2/53) NC and 9% (5/53) C regions had ratings under 3. After combining the NC and C contours for each of the 70 bladders, 66% (46/70) had quality ratings of 4 or above. Only 6% (4/70) had ratings under 3. The average quality rating was 3.8±0.7. The results demonstrate the potential of CLASS for automated segmentation of the bladder.
我们正在开发一种用于CTU上膀胱分割的联合水平集分析与分割系统（CLASS），这是膀胱癌计算机辅助诊断的关键组成部分。膀胱分割面临的一个挑战是存在无对比剂（NC）区域和充满静脉内对比剂（C）的区域。根据CTU中膀胱的特征，CLASS旨在执行诸如预处理、初始分割、3D和2D水平集分割以及后处理等多项任务。CLASS分别对膀胱的NC和C区域进行分割。在后处理阶段，基于NC和C轮廓的并集获得最终轮廓。对70个膀胱进行了分割。在这70个膀胱中，31个含有病变，24个有壁增厚，15个正常。通过以5分制（1 =“非常差”，3 =“一般”，5 =“优秀”）对轮廓质量进行评级来评估CLASS的性能。12个完全无对比剂（NC）和5个完全充满对比剂（C）的膀胱轮廓的平均质量评级分别为3.3±1.0和3.4±0.5。53个部分充满对比剂的膀胱的53个NC和53个C区域的平均质量评级分别为4.0±0.7和4.0±1.0。87%（46/53）的NC区域和77%（41/53）的C区域质量评级为4或更高。只有4%（2/53）的NC区域和9%（5/53）的C区域评级低于3。在将70个膀胱中每个膀胱的NC和C轮廓合并后，66%（46/70）的质量评级为4或更高。只有6%（4/70）的评级低于3。平均质量评级为3.8±0.7。结果证明了CLASS在膀胱自动分割方面的潜力。
