Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling
Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling
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DOI：
10.1109/tmi.2015.2437192
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发表时间：
2015-08-01
影响因子：
10.6
通讯作者：
Kadoury, Samuel
中科院分区：
文献类型：
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作者：
De Leener, Benjamin;Cohen-Adad, Julien;Kadoury, Samuel
量化神经退行性疾病和创伤性疾病中的脊髓（SC）萎缩为临床医生带来了重要的诊断和预后信息。我们最近开发了PropSeg方法，该方法允许在不同类型的MRI对比度上快速、准确和自动分割SC（例如，T-1、T-2和T-3加权序列）和任何视场。然而，比较测量从SC受试者之间的阻碍缺乏一个通用的坐标系的SC。在本文中，我们提出了一个新的框架相结合的PropSeg和椎体水平识别方法，从而使直接的跨和受试者内比较SC测量大型队列研究以及纵向研究。我们的分割方法是基于管状变形模型的多分辨率传播。结合自动椎间盘识别方法，我们的分割管道提供了SC和椎管的定量指标，例如基于椎骨水平的通用坐标系中的横截面积和体积。该框架在17名健康受试者和1名SC损伤患者上进行了验证，以对抗手动分割。结果已与现有的主动表面方法进行了比较，并显示高的局部和全局精度SC和椎管（骰子系数=0.91 +/- 0.02）分割。具有用于SC分割和基于椎骨的标准化的鲁棒且自动的框架打开了在大型队列中无偏差测量SC萎缩的大门。
Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g., T-1-, T-2- and T-3-weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter-and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 +/- 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts.