title: Groupwise multi-atlas segmentation of the spinal cord's internal structure.
abstract: 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.
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA. Electronic address: andrew.j.asman@vanderbilt.edu.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Asman
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Frederick W
authors.initials: FW
authors.lastname: Bryan
authors.affiliation: Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Seth A
authors.initials: SA
authors.lastname: Smith
authors.affiliation: Translational Neuroradiology Unit, National Institutes of Health, Bethesda, MD 37235, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Reich
authors.affiliation: Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Bennett A
authors.initials: BA
authors.lastname: Landman
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013116:Spinal Cord / Q000473:pathology*; D013122:Spinal Diseases / Q000473:pathology*; D013382:Subtraction Technique
keywords: Cervical spinal cord segmentation; Groupwise registration; Multi-atlas segmentation; Spinal cord internal structure