Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada.
Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada; Department of Computer Engineering, Polytechnique Montréal, Montreal, QC, Canada.
Neuroimage. 2014 Sep;98:528-36. doi: 10.1016/j.neuroimage.2014.04.051. Epub 2014 Apr 27.
Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord.
脊髓分割提供了萎缩的度量，并通过受试者间的对应关系促进了组分析。自动执行此过程可以实现高通量的研究，并最大限度地减少用户偏差。虽然存在几种自动分割方法，但它们通常在图像对比度和视野方面受到限制。本文提出了一种新的自动分割方法（PropSeg），该方法具有鲁棒性、准确性和速度方面的优势。该算法基于变形模型的传播，分为三个部分：首先，初始化步骤使用圆形 Hough 变换在起始平面的头侧和尾侧的多个轴向切片上检测脊髓的位置和方向，并构建初始的椭圆形管状网格。其次，低分辨率的变形模型沿着脊髓传播。为了处理脊髓和脑脊液之间对比度水平变化很大的问题，在每次迭代时，变形与局部对比度噪声自适应相结合。最后，在传播的网格上应用细化过程和全局变形，以提供脊髓的精确分割。在 15 名健康受试者和 2 名脊髓损伤患者中，使用整个脊髓的 T1-和 T2-加权图像以及多回波 T2*-加权图像对其进行了验证。我们的方法与手动分割和主动表面方法进行了比较。结果表明，所有磁共振序列的精度都很高。T1-和 T2-加权队列的 Dice 系数为 0.9，T2*-加权图像的 Dice 系数为 0.86。该方法在普通计算机上的运行时间不到 1 分钟，可以用于量化整个脊髓的横截面积等形态特征。
