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基于一维归一化模板匹配的脊髓分割:一种分析高级磁共振成像数据的新型定量技术。

Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data.

作者信息

Cadotte Adam, Cadotte David W, Livne Micha, Cohen-Adad Julien, Fleet David, Mikulis David, Fehlings Michael G

机构信息

Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.

Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada; Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.

出版信息

PLoS One. 2015 Oct 7;10(10):e0139323. doi: 10.1371/journal.pone.0139323. eCollection 2015.

Abstract

Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.

摘要

脊髓分割是一个正在发展的研究领域,旨在辅助高级磁共振成像(MRI)的处理和解读。例如,高分辨率三维体积可以进行分割,以提供脊髓萎缩的测量值。由于MRI对比度的多样性和人体解剖结构的差异,脊髓分割具有一定难度。在本研究中,我们提出了一种基于一维模板匹配的脊髓分割新方法,并提供了几个可用于与其他分割方法进行比较的指标。一组来自10名受试者的真实数据由两名不同的评分者进行了手动分割。这些真实数据构成了分割算法的基础。要求用户在新图像上手动初始化脊髓中心线,耗时不到一分钟。使用模板匹配对新的脊髓进行分割,并基于分割内的多个质心计算出一条优化的中心线。计算了沿脊髓的弧长距离和横截面积。通过比较两名手动评分者(n = 10)进行评分者间验证。通过将两名手动评分者与半自动方法进行比较(n = 10)进行半自动验证。将半自动方法与其中一名评分者进行比较,对于10名受试者,得到的骰子系数为0.91±0.02,脊髓中心线之间的平均距离为0.32±0.08毫米,豪斯多夫距离为1.82±0.33毫米。与评分者间手动分割相比,半自动方法与手动分割在横截面积的绝对变化方面具有可比性。结果表明,对于大多数分割指标,这种新颖的分割方法与手动评分者的表现相当。它为研究脊髓疾病以及在个体病例和受试者队列中定量跟踪脊髓内的变化提供了一种新方法。

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