论文标题
心脏磁共振图像中左心室的自动分割
Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images
论文作者
论文摘要
心脏磁共振成像MRI扫描中左心室的分割使心脏病学家能够计算左心室的体积，然后计算其射血分数。射血分数是一种测量，表达了每次收缩的血液的百分比。心脏病学家经常使用射血分数来确定自己的心脏功能。我们提出了用于检测的多尺度模板匹配技术和MR图像中左心室自动分割的椭圆活动盘。椭圆活动光盘相对于其定义盘的五个自由参数优化了局部能量函数。梯度下降用于将能量函数与格林定理一起最小化，以优化计算费用。我们报告了320次扫描的验证，其中包含5,273个带注释的切片，这些切片可通过多中心，多供应商和多疾病性心脏细分（M＆M）挑战公开获得。我们在89.63％的病例中成功定位了左心室，舒张切片上的骰子系数为0.873，收缩片切片上的骰子系数为0.770。提出的技术基于传统的图像处理技术，其性能与深度学习技术相当。
Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. The ejection fraction is a measurement that expresses the percentage of blood leaving the heart with each contraction. Cardiologists often use ejection fraction to determine one's cardiac function. We propose multiscale template matching technique for detection and an elliptical active disc for automated segmentation of the left ventricle in MR images. The elliptical active disc optimizes the local energy function with respect to its five free parameters which define the disc. Gradient descent is used to minimize the energy function along with Green's theorem to optimize the computation expenses. We report validations on 320 scans containing 5,273 annotated slices which are publicly available through the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M&Ms) Challenge. We achieved successful localization of the left ventricle in 89.63% of the cases and a Dice coefficient of 0.873 on diastole slices and 0.770 on systole slices. The proposed technique is based on traditional image processing techniques with a performance on par with the deep learning techniques.
