Robust, accurate and fast automatic segmentation of the spinal cord
Robust, accurate and fast automatic segmentation of the spinal cord
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DOI：
10.1016/j.neuroimage.2014.04.051
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发表时间：
2014-09-01
期刊：
影响因子：
5.7
通讯作者：
Cohen-Adad, Julien
中科院分区：
文献类型：
--
作者：
De Leener, Benjamin;Kadoury, Samuel;Cohen-Adad, Julien
脊髓分割提供了萎缩的措施，并通过主体间锥体反射促进了群体分析。这一过程的自动化使研究具有较大的吞吐量，并最大限度地减少用户偏见。尽管存在几种自动分割方法，但它们往往受到图像对比度和视场的限制。本文提出了一种鲁棒性、准确率和速度都得到优化的自动分割方法（PropSeg）。该算法基于可变形模型的传播，分为三部分：首先，初始化步骤在起始平面的吻侧和尾侧多个轴向切片上使用圆形霍夫变换检测脊髓的位置和方向，并构建初始椭圆管状模型；其次，沿脊髓传播低分辨率可变形模型。为了处理脊髓和脑脊液之间高度可变的对比度水平，在每次迭代中，变形与局部对比度-噪声适应相结合。第三，对传播网格进行细化处理和全局变形，以提供准确的脊髓分割。在健康受试者和2例脊髓损伤患者中进行验证，使用整个脊髓的T-2加权图像和多回声T-2*加权图像。将该方法与人工分割法和活动曲面法进行了比较。结果表明，所有的MR序列都具有较高的精度。对于T-1和T-2加权的队列，骰子系数为0.9，对于T-2*加权的图像，骰子系数为0.86。所提出的方法在普通计算机上运行不到1分钟，可用于量化形态学特征，如沿整个脊髓的横截面积。2014爱思唯尔公司版权所有
Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject cone spondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. P though several automatic segmentation methods exist, they are often restricted in terms of image contrast am field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, at_ curacy and speed. The algorithm is based on the propagation of a deformable model and is divided into thre parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough trans form on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mer: Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variab contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a loccontrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applie on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in healthy subjects and two patients with spinal cord injury, using and T-2-weighted images of the entire spini cord and on multiecho T-2*-weighted images. Our method was compared against manual segmentation am against an active surface method. Results show high precision for all the MR sequences. Dice coefficients we 0.9 for the T-1- and T-2-weighted cohorts and 0.86 for the T-2*-weighted images. The proposed method runs less than 1 min on a normal computer and can be used to quantify morphological features such as crosr sectional area along the whole spinal cord. 2014 Elsevier Inc. All rights reserve