Detection of urinary bladder mass in CT urography with SPAN.
Detection of urinary bladder mass in CT urography with SPAN.
复制标题
SPAN CT 尿路造影检测膀胱肿块。
DOI：
10.1118/1.4922503
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发表时间：
2015
期刊：
影响因子：
3.8
通讯作者：
Zhou,Chuan
中科院分区：
文献类型：
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作者：
Cha,Kenny;Hadjiiski,Lubomir;Chan,Heang-Ping;Cohan,RichardH;Caoili,ElaineM;Zhou,Chuan
目的研制一种基于CT尿路造影（CTU）的膀胱癌计算机辅助检测系统。在这项研究中，作者专注于开发一种系统，用于检测膀胱造影剂增强（C）区域内的全部或部分肿块。本研究采集膀胱（C区）：35名患者用于训练集（39个恶性病变，7个良性病变），35名患者用于测试集（49个恶性病变，4个良性病变）。CTU图像中的膀胱使用作者的联合水平集分析和分割系统自动分割，他们专门开发该系统来分割膀胱。通过最大强度投影生成膀胱C区的闭合轮廓，该最大强度投影使用这样的性质，即膀胱中的相关分层造影剂将由于重力而沿着沿着所有CTU切片一致地填充到相同水平。使用作者的Straightened Quartered Analysis（SPAN）方法发现C区轮廓内的潜在病变候选者。SPAN将膀胱壁转换为拉直的厚度轮廓，在轮廓上标记可疑像素，并将它们聚类到感兴趣的区域中以识别潜在的病变候选者。采用自初始化的级联水平集分割方法对候选区域进行自动分割。从分割的病变中自动提取了23个形态学特征。训练集用于使用具有留一法的单纯形优化来确定这些特征的最佳子集。设计了一个线性判别分类器，用于膀胱病变和假阳性的分类。在独立的测试集上，通过自由响应接收器操作特性analysis.ResultsAt预筛选步骤，作者的系统实现了84.4%的灵敏度，平均每例4.3假阳性（FPs/case）的训练集，和84.9%的灵敏度，5.4 FPs/case的测试集。在使用所选特征进行线性判别分析（LDA）分类后，FP率在训练集提高到2.5 FP/例，在测试集提高到4.3 FP/例，而没有遗漏额外的真实病变。通过改变LDA评分的阈值（2.5 FP/例），训练集和测试集的灵敏度分别为84.4%和81.1%。在1.7 FPs/例，灵敏度下降到77.8%和75.5%，respectively.ConclusionsThe结果证明了作者的方法的可行性检测膀胱病变完全或部分浸没在对比增强区域的CTU。
PurposeThe authors are developing a computer‐aided detection system for bladder cancer on CT urography (CTU). In this study, the authors focused on developing a system for detecting masses fully or partially within the contrast‐enhanced (C) region of the bladder.MethodsWith IRB approval, a data set of 70 patients with biopsy‐proven bladder lesions fully or partially immersed within the contrast‐enhanced region (C region) of the bladder was collected for this study: 35 patients for the training set (39 malignant, 7 benign lesions) and 35 patients for the test set (49 malignant, 4 benign lesions). The bladder in the CTU images was automatically segmented using the authors’ conjoint level set analysis and segmentation system, which they developed specifically to segment the bladder. A closed contour of the C region of the bladder was generated by maximum intensity projection using the property that the dependently layering contrast material in the bladder will be filled consistently to the same level along all CTU slices due to gravity. Potential lesion candidates within the C region contour were found using the authors’ Straightened Periphery ANalysis (SPAN) method. SPAN transforms a bladder wall to a straightened thickness profile, marks suspicious pixels on the profile, and clusters them into regions of interest to identify potential lesion candidates. The candidate regions were automatically segmented using the authors’ autoinitialized cascaded level set segmentation method. Twenty‐three morphological features were automatically extracted from the segmented lesions. The training set was used to determine the best subset of these features using simplex optimization with the leave‐one‐out case method. A linear discriminant classifier was designed for the classification of bladder lesions and false positives. The detection performance was evaluated on the independent test set by free‐response receiver operating characteristic analysis.ResultsAt the prescreening step, the authors’ system achieved 84.4% sensitivity with an average of 4.3 false positives per case (FPs/case) for the training set, and 84.9% sensitivity with 5.4 FPs/case for the test set. After linear discriminant analysis (LDA) classification with the selected features, the FP rate improved to 2.5 FPs/case for the training set, and 4.3 FPs/case for the test set without missing additional true lesions. By varying the threshold for the LDA scores, at 2.5 FPs/case, the sensitivities were 84.4% and 81.1% for the training and test sets, respectively. At 1.7 FPs/case, the sensitivities decreased to 77.8% and 75.5%, respectively.ConclusionsThe results demonstrate the feasibility of the authors’ method for detection of bladder lesions fully or partially immersed in the contrast‐enhanced region of CTU.