Chen Wenan, Belle Ashwin, Cockrell Charles, Ward Kevin R, Najarian Kayvan
Department of Biostatistics, Virginia Commonwealth University.
J Vis Exp. 2013 Apr 13(74):3871. doi: 10.3791/3871.
In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.
在本文中，我们提出了一个主要基于计算机断层扫描（CT）图像的自动化系统，该系统由两个主要部分组成：中线移位估计和颅内压（ICP）预筛查系统。为了估计中线移位，首先基于颅骨的对称性和脑部CT扫描中的解剖特征对理想中线进行估计。然后，从CT扫描中分割出脑室，并通过形状匹配将其用作识别实际中线的指导。这些过程模仿了医生的测量过程，并且在评估中显示出了有希望的结果。在第二个部分中，提取了更多与颅内压相关的特征，例如纹理信息、CT扫描中的出血量以及其他记录的特征，如年龄、损伤严重程度评分等，也被纳入以估计颅内压。使用包括特征选择和分类在内的机器学习技术，如支持向量机（SVM），利用RapidMiner构建预测模型。预测评估显示了该模型的潜在有用性。估计的理想中线移位和预测的颅内压水平可作为医生进行决策的快速预筛查步骤，以便推荐是否进行有创颅内压监测。
