CT图像自动化处理评估泌尿系结石成分
Estimation of urinary stone composition by automated processing of CT images
摘要：
本文的目的是开发一种自动化工具，用于常规临床实践，以根据所有组成体素的密度从CT图像中估计泌尿系结石成分。共118个结石的组成已确定的红外光谱被放置在螺旋CT扫描仪。进行了标准采集、低剂量和高剂量采集。自动选择构成每个结石的所有体素。为了最大限度地减少部分体积效应，创建了一个评估每个体素周围密度变化的相异性指数：根据均匀区域的体素密度建立结石成分。在52%的病例中确定了结石成分。每种化合物的灵敏度为：尿酸：65%，鸟粪石：19%，胱氨酸：78%，碳磷灰石：33.5%，二水草酸钙：57%，一水草酸钙：66.5%，透钙磷石：75%。低剂量采集并没有降低性能（P < 0.05）。这种完全自动化的方法消除了放射科医生对图像的手动干预，同时提供相同的性能，包括低剂量协议。
英文摘要：
The objective of this article was developing an automated tool for routine clinical practice to estimate urinary stone composition from CT images based on the density of all constituent voxels. A total of 118 stones for which the composition had been determined by infrared spectroscopy were placed in a helical CT scanner. A standard acquisition, low-dose and high-dose acquisitions were performed. All voxels constituting each stone were automatically selected. A dissimilarity index evaluating variations of density around each voxel was created in order to minimize partial volume effects: stone composition was established on the basis of voxel density of homogeneous zones. Stone composition was determined in 52% of cases. Sensitivities for each compound were: uric acid: 65%, struvite: 19%, cystine: 78%, carbapatite: 33.5%, calcium oxalate dihydrate: 57%, calcium oxalate monohydrate: 66.5%, brushite: 75%. Low-dose acquisition did not lower the performances (P < 0.05). This entirely automated approach eliminates manual intervention on the images by the radiologist while providing identical performances including for low-dose protocols.
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