基于直方图的腹盆腔计算机断层扫描中静脉造影剂的辨别
Histogram-Based Discrimination of Intravenous Contrast in Abdominopelvic Computed Tomography.
作者信息
Cheng Phillip M
机构信息
From the Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA.
出版信息
J Comput Assist Tomogr. 2016 Mar-Apr;40(2):234-7. doi: 10.1097/RCT.0000000000000361.
OBJECTIVE
The aim of this study was to evaluate the accuracy of fully automated machine learning methods for detecting intravenous contrast in computed tomography (CT) studies of the abdomen and pelvis.
METHODS
A set of 591 labeled CT image volumes of the abdomen and pelvis was obtained from 5 different CT scanners, of which 434 (73%) were performed with intravenous contrast. A stratified split of this set was performed into training and test sets of 443 and 148 studies, respectively. Subsequently, support vector machine and logistic regression classifiers were trained using 5-fold cross-validation for parameter optimization.
RESULTS
The best in-sample performance was seen with a support vector machine classifier with a χ kernel (98.9% accuracy); however, test set performance was similar across the trained classifiers, with 95% to 97% accuracy.
CONCLUSIONS
Histogram-based automated classifiers for the presence of intravenous contrast are accurate and may be useful for verifying the accurate labeling of the presence of intravenous contrast in CT body studies.
目的
本研究旨在评估全自动机器学习方法在腹部和盆腔计算机断层扫描（CT）研究中检测静脉造影剂的准确性。
方法
从5台不同的CT扫描仪获取了一组591个标记的腹部和盆腔CT图像体积，其中434个（73%）是在静脉注射造影剂的情况下进行的。对该组进行分层划分，分别分为443项和148项研究的训练集和测试集。随后，使用5折交叉验证训练支持向量机和逻辑回归分类器进行参数优化。
结果
使用具有χ核的支持向量机分类器观察到最佳的样本内性能（准确率98.9%）；然而，训练后的分类器在测试集上的性能相似，准确率为95%至97%。
结论
基于直方图的静脉造影剂存在自动分类器准确，可能有助于验证CT身体研究中静脉造影剂存在的准确标记。
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