论文标题
通过形状约束的多级CNN在X射线图像上精确的脊柱侧脊柱椎体位置定位
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
论文作者
论文摘要
椎骨地标定位是与脊柱相关的临床应用的关键步骤，它需要检测17个椎骨的角点。但是，邻居的地标通常会互相打扰，椎骨的同质外观，这使得椎骨地标定位极为困难。在本文中，我们提出了多阶段级联的卷积神经网络（CNN），将单个任务分为两个顺序的步骤，即中心点定位到大致定位椎骨的17个中心点，而角点定位到找到每个椎骨的4个角点，而没有其他人分心。每个步骤中的地标通过通过级联的CNN回归偏移来逐渐位于一组初始化点。主成分分析（PCA）用于在抵销回归中保留形状约束，以抵抗椎骨的相互吸引力。我们在AASCE数据集上评估我们的方法，该数据集由609个紧密的脊柱前X射线图像组成，每个图像包含17个由胸部和腰椎组成的椎骨，用于脊柱形状表征。实验结果表明，我们对椎骨地标定位的表现优于其他最先进的表现，相对误差从3.2e-3降低到7.2e-4。
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor landmarks often disturb each other for the homogeneous appearance of vertebrae, which makes vertebral landmark localization extremely difficult. In this paper, we propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps, i.e., center point localization to roughly locate 17 center points of vertebrae, and corner point localization to find 4 corner points for each vertebra without distracted by others. Landmarks in each step are located gradually from a set of initialized points by regressing offsets via cascaded CNNs. Principal Component Analysis (PCA) is employed to preserve a shape constraint in offset regression to resist the mutual attraction of vertebrae. We evaluate our method on the AASCE dataset that consists of 609 tight spinal anterior-posterior X-ray images and each image contains 17 vertebrae composed of the thoracic and lumbar spine for spinal shape characterization. Experimental results demonstrate our superior performance of vertebral landmark localization over other state-of-the-arts with the relative error decreasing from 3.2e-3 to 7.2e-4.
