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首页> 外文期刊>Journal of Digital Imaging >Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis
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Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis

机译：非造影CT图像上尿石和血管钙化的鉴别：使用计算机辅助诊断的初步经验

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The purpose of this study was to develop methods for the differentiation of urinary stones and vascular calcifications using computer-aided diagnosis (CAD) of non-contrast computed tomography (CT) images. From May 2003 to February 2004, 56 patients that underwent a pre-contrast CT examination and subsequently diagnosed as ureter stones were included in the study. Fifty-nine ureter stones and 53 vascular calcifications on pre-contrast CT images of the patients were evaluated. The shapes of the lesions including disperseness, convex hull depth, and lobulation count were analyzed for patients with ureter stones and vascular calcifications. In addition, the internal textures including edge density, skewness, difference histogram variation (DHV), and the gray-level co-occurrence matrix moment were also evaluated for the patients. For evaluation of the diagnostic accuracy of the shape and texture features, an artificial neural network (ANN) and receiver operating characteristics curve (ROC) analyses were performed. Of the several shape factors, disperseness showed a statistical difference between ureter stones and vascular calcifications (p < 0.05). For the internal texture features, skewness and DHV showed statistical differences between ureter stones and vascular calcifications (p < 0.05). The performance of the ANN was evaluated by examining the area under the ROC curves (AUC, A z). The A z value was 0.85 for the shape parameters and 0.88 for the texture parameters. In this study, several parameters regarding shape and internal texture were statistically different between ureter stones and vascular calcifications. The use of CAD would make it possible to differentiate ureter stones from vascular calcifications by a comparison of these parameters.
机译：这项研究的目的是开发使用非造影计算机断层扫描（CT）图像的计算机辅助诊断（CAD）鉴别尿路结石和血管钙化的方法。从2003年5月至2004年2月，本研究包括56例接受了CT对比检查并随后被诊断为输尿管结石的患者。在患者的对比前CT图像上评估了59个输尿管结石和53个血管钙化。对输尿管结石和血管钙化患者的病变形状进行了分析，包括弥散度，凸壳深度和小叶计数。此外，还评估了患者的内部纹理，包括边缘密度，偏度，差异直方图变化（DHV）和灰度共现矩阵矩。为了评估形状和纹理特征的诊断准确性，进行了人工神经网络（ANN）和接收器工作特性曲线（ROC）分析。在几个形状因素中，分散性显示输尿管结石和血管钙化之间存在统计学差异（p <0.05）。对于内部纹理特征，偏斜度和DHV显示输尿管结石和血管钙化之间的统计学差异（p <0.05）。通过检查ROC曲线下的面积（AUC，A z ）来评估ANN的性能。形状参数的A z 值为0.85，纹理参数的A z 值为0.88。在这项研究中，输尿管结石和血管钙化之间有关形状和内部纹理的几个参数在统计学上是不同的。通过使用CAD，可以通过比较这些参数来区分输尿管结石和血管钙化。

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