CT urography: segmentation of urinary bladder using CLASS with local contour refinement.
CT urography: segmentation of urinary bladder using CLASS with local contour refinement.
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DOI：
10.1088/0031-9155/59/11/2767
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发表时间：
2014-06-07
影响因子：
3.5
通讯作者：
Zhou C
中科院分区：
文献类型：
--
作者：
Cha K;Hadjiiski L;Chan HP;Caoili EM;Cohan RH;Zhou C
我们正在开发一个计算机化的系统，膀胱分割CT尿路造影（CTU），作为计算机辅助检测膀胱癌的一个重要组成部分。填充有静脉造影剂和没有造影剂的区域的存在对膀胱分割提出了挑战。在此之前，我们提出了一个联合水平集分析和分割系统（CLASS）。在膀胱部分填充造影剂的情况下，CLASS分别分割非造影剂（NC）区域和造影剂填充（C）区域，并自动连接NC和C区域轮廓;但是，NC和C区域轮廓的不准确性可能导致连接轮廓排除膀胱的部分。为了缓解这个问题，我们实现了一个局部轮廓细化（LCR）的方法，利用模型引导细化（MGR）和能量驱动波前传播（EDWP）。如果C区域中的水平集传播由于对比度的显著不均匀性而过早停止，则MGR传播C区域轮廓。具有正则化能量的EDWP进一步将联合轮廓传播到正确的膀胱边界。EDWP使用能量的变化、轮廓的平滑度标准和先前的切片轮廓来确定何时停止传播，遵循从训练中导出的决策规则。本研究收集了173例病例的数据集：训练集81例（42个病变，21个壁增厚，18个正常膀胱），测试集92例（43个病变，36个壁增厚，13个正常膀胱）。对于所有病例，获得3D手分割轮廓作为参考标准，并用于评价计算机分割精度。对于具有LCR的CLASS，训练集的平均体积相交率、平均体积误差、绝对平均体积误差、平均最小距离和Jaccard指数分别为84.2± 11.4%、8.2± 17.4%、13.0± 14.1%、3.5±1.9 mm和78.8± 11.6%，而训练集的平均体积相交率、平均体积误差、绝对平均体积误差、平均最小距离和Jaccard指数分别为78.0± 14.7%、16.4± 16.9%、对于测试集，分别为18.2± 15.0%、3.8±2.3 mm、73.8±13.4%。仅使用CLASS，训练集的相应值分别为75.1± 13.2%、18.7± 19.5%、22.5± 14.9%、4.3±2.2 mm、71.0± 12.6%，测试集的相应值分别为67.3± 14.3%、29.3± 15.9%、29.4± 15.6%、4.9±2.6 mm、65.0± 13.3%。对于训练集和测试集，两种方法在所有五个测量值上的差异都具有统计学显著性（p<0.001）。结果表明，具有LCR的CLASS用于膀胱分割的潜力。
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.
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DOI：
10.1109/titb.2012.2200496
发表时间：
2012-07
期刊：
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Duan C;Yuan K;Liu F;Xiao P;Lv G;Liang Z
通讯作者：
Liang Z
影响因子：
1.1
作者：
Akbar, SA;Mortele, KJ;Silverman, SG
通讯作者：
Silverman, SG
影响因子：
19.7
作者：
Park, Sung Bin;Kim, Jeong Kon;Cho, Kyoung-Sik
通讯作者：
Cho, Kyoung-Sik
影响因子：
3.8
作者：
Street, Ethan;Hadjiiski, Lubomir;Chan, Heang-Ping
通讯作者：
Chan, Heang-Ping
影响因子：
3.8
作者：
Way, Ted W.;Hadjiiski, Lubomir M.;Zhou, Chuan
通讯作者：
Zhou, Chuan