Linguraru Marius George, Wang Shijun, Shah Furhawn, Gautam Rabindra, Peterson James, Linehan W, Summers Ronald M
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6679-82. doi: 10.1109/IEMBS.2009.5334012.
In clinical practice, renal cancer diagnosis is performed by manual quantifications of tumor size and enhancement, which are time consuming and show high variability. We propose a computer-assisted clinical tool to assess and classify renal tumors in contrast-enhanced CT for the management and classification of kidney tumors. The quantification of lesions used level-sets and a statistical refinement step to adapt to the shape of the lesions. Intra-patient and inter-phase registration facilitated the study of lesion enhancement. From the segmented lesions, the histograms of curvature-related features were used to classify the lesion types via random sampling. The clinical tool allows the accurate quantification and classification of cysts and cancer from clinical data. Cancer types are further classified into four categories. Computer-assisted image analysis shows great potential for tumor diagnosis and monitoring.
在临床实践中，肾癌诊断通过手动量化肿瘤大小和强化程度来进行，这既耗时又具有高度变异性。我们提出一种计算机辅助临床工具，用于在对比增强CT中评估和分类肾肿瘤，以实现肾肿瘤的管理和分类。病变的量化使用水平集和统计细化步骤来适应病变的形状。患者内和相际配准有助于研究病变强化。从分割的病变中，通过随机抽样使用与曲率相关特征的直方图来分类病变类型。该临床工具能够根据临床数据准确量化和分类囊肿与癌症。癌症类型进一步分为四类。计算机辅助图像分析在肿瘤诊断和监测方面显示出巨大潜力。
