使用 CLASS 对 CTU 中的膀胱进行分割。
Urinary bladder segmentation in CT urography (CTU) using CLASS.
机构信息
Department of Radiology, the University of Michigan, Ann Arbor, Michigan 48109-0904.
出版信息
Med Phys. 2013 Nov;40(11):111906. doi: 10.1118/1.4823792.
PURPOSE
The authors are developing a computerized system for bladder segmentation on CTU, as a critical component for computer aided diagnosis of bladder cancer.
METHODS
A challenge for bladder segmentation is the presence of regions without contrast (NC) and filled with intravenous contrast (C). The authors have designed a Conjoint Level set Analysis and Segmentation System (CLASS) specifically for this application. CLASS performs a series of image processing tasks: preprocessing, initial segmentation, 3D and 2D level set segmentation, and postprocessing, designed according to the characteristics of the bladder in CTU. The NC and the C regions of the bladder were segmented separately in CLASS. The final contour is obtained in the postprocessing stage by the union of the NC and C contours. With Institutional Review Board (IRB) approval, the authors retrospectively collected 81 CTU scans, in which 40 bladders contained lesions, 26 contained diffuse wall thickening, and 15 were considered to be normal. The bladders were segmented by CLASS and the performance was assessed by rating the quality of the contours on a 10-point scale (1 = "very poor," 5 = "fair," 10 = "perfect"). For 30 bladders, 3D hand-segmented contours were obtained and the segmentation accuracy of CLASS was evaluated and compared to that of a single level set method in terms of the average minimum distance, average volume intersection ratio, average volume error and Jaccard index.
RESULTS
Of the 81 bladders, the average quality rating for CLASS was 6.5 ± 1.3. Thirty nine bladders were given quality ratings of 7 or above. Only five bladders had ratings under 5. The average minimum distance, average volume intersection ratio, average volume error, and average Jaccard index for CLASS were 3.5 ± 1.3 mm, (79.0 ± 8.2)%, (16.1 ± 16.3)%, and (75.7 ± 8.4)%, respectively, and for the single level set method were 5.2 ± 2.6 mm, (78.8 ± 16.3)%, (8.3 ± 33.1)%, (71.0 ± 15.4)%, respectively.
CONCLUSIONS
The results demonstrate the potential of CLASS for segmentation of the bladder.
目的
作者正在开发一种 CTU 上的膀胱分割计算机系统，作为膀胱癌计算机辅助诊断的关键组成部分。
方法
膀胱分割的一个挑战是存在无对比（NC）和充满静脉对比（C）的区域。作者专门为此应用设计了联合水平集分析和分割系统（CLASS）。CLASS 执行一系列图像处理任务：预处理、初始分割、3D 和 2D 水平集分割以及后处理，这些任务是根据 CTU 中膀胱的特点设计的。NC 和 C 区域的膀胱在 CLASS 中分别进行分割。在后处理阶段，通过将 NC 和 C 轮廓的联合来获得最终轮廓。在机构审查委员会（IRB）的批准下，作者回顾性地收集了 81 例 CTU 扫描，其中 40 例膀胱包含病变，26 例膀胱弥漫性壁增厚，15 例被认为是正常的。膀胱由 CLASS 分割，通过 10 分制（1=“非常差”，5=“一般”，10=“完美”）对轮廓质量进行评分来评估性能。对于 30 例膀胱，获得了 3D 手动分割轮廓，并根据平均最小距离、平均体积交集比、平均体积误差和 Jaccard 指数评估 CLASS 的分割准确性，并与单水平集方法进行比较。
结果
81 例膀胱中，CLASS 的平均质量评分为 6.5±1.3。39 例膀胱的评分在 7 分以上。只有 5 例膀胱的评分低于 5。CLASS 的平均最小距离、平均体积交集比、平均体积误差和平均 Jaccard 指数分别为 3.5±1.3mm、（79.0±8.2）%、（16.1±16.3）%和（75.7±8.4）%，而单水平集方法的平均最小距离、平均体积交集比、平均体积误差和平均 Jaccard 指数分别为 5.2±2.6mm、（78.8±16.3）%、（8.3±33.1）%和（71.0±15.4）%。
结论
结果表明 CLASS 具有分割膀胱的潜力。
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