Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Med Image Anal. 2014 Apr;18(3):460-71. doi: 10.1016/j.media.2014.01.003. Epub 2014 Feb 5.
The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal cord internal structure (i.e., gray matter vs. white matter) is critical for assessment of therapeutic impacts and determining prognosis of relevant conditions. Fortunately, new magnetic resonance imaging (MRI) sequences enable clinical study of the in vivo spinal cord's internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Additionally, due to the inter-subject variability exhibited on cervical MRI, typical deformable volumetric registrations perform poorly, limiting the applicability of a typical multi-atlas segmentation framework. Thus, to date, no automated algorithms have been presented for the spinal cord's internal structure. Herein, we present a novel slice-based groupwise registration framework for robustly segmenting cervical spinal cord MRI. Specifically, we provide a method for (1) pre-aligning the slice-based atlases into a groupwise-consistent space, (2) constructing a model of spinal cord variability, (3) projecting the target slice into the low-dimensional space using a model-specific registration cost function, and (4) estimating robust segmentation susing geodesically appropriate atlas information. Moreover, the proposed framework provides a natural mechanism for performing atlas selection and initializing the free model parameters in an informed manner. In a cross-validation experiment using 67 MR volumes of the cervical spinal cord, we demonstrate sub-millimetric accuracy, significant quantitative and qualitative improvement over comparable multi-atlas frameworks, and provide insight into the sensitivity of the associated model parameters.
脊髓是中枢神经系统的重要和脆弱组成部分。区分和定位脊髓内部结构(即灰质与白质)对于评估治疗效果和确定相关疾病的预后至关重要。幸运的是,新的磁共振成像(MRI)序列使临床研究活体脊髓的内部结构成为可能。然而,低对比噪声比、伪影和成像失真限制了在中枢神经系统其他部位开创的组织分割技术的适用性。此外,由于颈椎 MRI 上存在个体间的可变性,典型的可变形体积配准表现不佳,限制了典型多图谱分割框架的适用性。因此,迄今为止,还没有针对脊髓内部结构的自动化算法。在此,我们提出了一种新颖的基于切片的分组配准框架,用于稳健地分割颈椎脊髓 MRI。具体来说,我们提供了一种方法来:(1)将基于切片的图谱预对齐到分组一致的空间中;(2)构建脊髓可变性模型;(3)使用特定于模型的配准成本函数将目标切片投影到低维空间中;(4)使用测地线适当的图谱信息估计稳健的分割。此外,所提出的框架提供了一种自然的机制,用于以明智的方式执行图谱选择和初始化自由模型参数。在使用 67 个颈椎脊髓 MRI 体积进行的交叉验证实验中,我们证明了亚毫米级的准确性,与可比的多图谱框架相比有显著的定量和定性改善,并提供了对相关模型参数的敏感性的深入了解。