论文标题
从计算机断层扫描图像中的快速，健壮的股骨分割，用于患者特异性髋部骨折筛查
Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening
论文作者
论文摘要
骨质疏松症是一种常见的骨骼疾病，可增加骨折的风险。基于有限元分析的嘻哈风险筛选方法取决于分段计算机断层扫描（CT）图像；但是，当前的股骨分割方法需要对大数据集进行手动描述。在这里，我们提出了一个深层神经网络，用于对CT的近端股骨进行完全自动化，准确和快速分割。对一组1147个具有地面真相分割的近端股骨的评估表明，我们的方法易于筛选髋骨骨折风险筛查，这使我们更接近临床上可行的选择，用于筛查在危险中的髋部骨折易感性。
Osteoporosis is a common bone disease that increases the risk of bone fracture. Hip-fracture risk screening methods based on finite element analysis depend on segmented computed tomography (CT) images; however, current femur segmentation methods require manual delineations of large data sets. Here we propose a deep neural network for fully automated, accurate, and fast segmentation of the proximal femur from CT. Evaluation on a set of 1147 proximal femurs with ground truth segmentations demonstrates that our method is apt for hip-fracture risk screening, bringing us one step closer to a clinically viable option for screening at-risk patients for hip-fracture susceptibility.
