Sentence examples for using the unified segmentation from inspiring English sources

The sMRI data were spatially normalized to MNI space using the unified segmentation method in SPM8, segmented into GM, white matter, and cerebral spinal fluid (CSF).

For the comparisons of "modulated" images, the reformatted images were then segmented and normalised using the unified segmentation of SPM5 (FIL, Wellcome Trust Centre for Neuroimaging, London, UK).

Images were spatially normalized and segmented into different tissue classes using the unified segmentation model in SPM5 (Ashburner and Friston, 2005); default settings were used throughout.

Before analysis, volumes were preprocessed, and then spatially normalized and segmented into grey and white matter tissue using the unified segmentation model in SPM5 (Ashburner and Friston, 2005).

The images were segmented into gray and white matter images using the unified segmentation algorithm, a generative model that combines tissue segmentation, bias correction, and spatial normalization in the inversion of a single unified model.

Using SPM5, all available MR images (151 in total) were spatially normalized into Montreal Neurological Institute MNII) space and segmented into grey and white matter tissue classes using the unified segmentation method [ 28].

We used the unified segmentation algorithm [60], [61] implemented on SPM5 (Wellcome Department of Imaging Neuroscience, Institute of Neurology, UCL) to acquire images of gray matter, white matter and cerebrospinal fluid.

For each patient, the co-registered anatomical images and the demarcated lesion were normalized into MNI space using the unified normalization segmentation of SPM (http://www.fil.ion.ucl.ac.uk/spm) as shown in Fig. 3C.

They were then spatially normalized to the Montreal Neurological Institute space using the unified normalization-segmentation procedure in SPM5 with a resulting voxel size of 2 × 2 × 2 mm.

All functional volumes were spatially realigned, un-warped, and normalised to the MNI space using the unified normalisation-segmentation procedure, with resulting voxels size of 2 × 2 × 2 mm.

All functional volumes were spatially realigned, unwarped, and normalized to the MNI space using the unified normalization-segmentation procedure [Ashburner and Friston, 2005] with resulting voxels size of 2 × 2 × 2 mm.