Yao Jianhua, Burns Joseph E, Munoz Hector, Summers Ronald M
Radiology and Imaging Sciences Department, Clinical Center, The National Institutes of Health, Bethesda, MD 20892, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):509-16. doi: 10.1007/978-3-642-33454-2_63.
Assessment of trauma patients with multiple injuries can be one of the most clinically challenging situations dealt with by the radiologist. We propose a fully automated method to detect acute vertebral body fractures on trauma CT studies. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The extracted cortical shell is unwrapped onto a 2D map effectively converting a complex 3D fracture detection problem into a pattern recognition problem of fracture lines on a 2D plane. Twenty-eight features are computed for each fracture line and sent to a committee of support vector machines for classification. The system was tested on 18 trauma CT datasets and achieved 95.3% sensitivity and 1.7 false positives per case by leave-one-out cross validation.
对多发伤的创伤患者进行评估可能是放射科医生面临的最具临床挑战性的情况之一。我们提出了一种全自动方法，用于在创伤CT研究中检测急性椎体骨折。首先对脊柱进行分割并划分为各个椎体。然后使用可变形双表面模型提取椎体的皮质壳。将提取的皮质壳展开到二维地图上，有效地将复杂的三维骨折检测问题转化为二维平面上骨折线的模式识别问题。为每条骨折线计算28个特征，并将其发送到支持向量机委员会进行分类。该系统在18个创伤CT数据集中进行了测试，通过留一法交叉验证，灵敏度达到95.3%，每个病例的假阳性率为1.7。
