Automatic Annotation of Radiological Observations in Liver CT Images
Automatic Annotation of Radiological Observations in Liver CT Images
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发表时间：
2012
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通讯作者：
Francisco Gimenez;Jiajing Xu;Yi Liu;T. Liu;C. Beaulieu;D. Rubin;S. Napel
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作者：
Francisco Gimenez;Jiajing Xu;Yi Liu;T. Liu;C. Beaulieu;D. Rubin;S. Napel
我们的目标是预测放射性观测使用计算机衍生的成像特征提取的计算机断层扫描（CT）图像。我们创建了一个由79个CT图像组成的数据集，其中包含由放射科医生使用76个语义术语的受控词汇识别和注释的肝脏病变。提取了描述强度、纹理、形状和边缘清晰度的计算衍生特征。将传统逻辑回归与L（1）-正则化逻辑回归（LASSO）进行比较，以便使用计算特征预测放射学观察结果。该方法进行了评价留一交叉验证。信息放射学观察，如病变增强，多血管衰减，和均匀的保留预测计算功能。通过利用计算和语义特征之间的关系，这种方法可以导致更准确和有效的放射学报告。
We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in order to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hypervascular attenuation, and homogeneous retention were predicted well by computational features. By exploiting relationships between computational and semantic features, this approach could lead to more accurate and efficient radiology reporting.