We wish to thank all those at the CHDI/High Q Foundation responsible for the TRACK-HD study; in particular, Beth Borowsky, Allan Tobin, Daniel van Kammen, Ethan Signer, and Sherry Lifer. The authors also wish to extend their gratitude to the TRACK-HD study participants and their families. This work was undertaken at UCLH/UCL, which received a proportion of funding from the Department of Health&#39;s National Institute for Health Research Biomedical Research Centres funding scheme. S.J.T. acknowledges support of the National Institute for Health Research through the Dementias and Neurodegenerative Research Network, DeNDRoN.
TRACK-HD Investigators: 
C. Campbell, M. Campbell, I. Labuschagne, C. Milchman, J. Stout, Monash University, Melbourne, VIC, Australia; A. Coleman, R. Dar Santos, J. Decolongon, B. R. Leavitt, A. Sturrock, University of British Columbia, Vancouver, BC, Canada; A. Durr, C. Jauffret, D. Justo, S. Lehericy, C. Marelli, K. Nigaud, R. Valabr&#232;gue, ICM Institute, Paris, France; N. Bechtel, S. Bohlen, R. Reilmann, University of M&#252;nster, M&#252;nster, Germany; B. Landwehrmeyer, University of Ulm, Ulm, Germany; S. J. A. van den Bogaard, E. M. Dumas, J. van der Grond, E. P. &#39;t Hart, R. A. Roos, Leiden University Medical Center, Leiden, Netherlands; N. Arran, J. Callaghan, D. Craufurd, C. Stopford, University of Manchester, Manchester, United Kingdom; D. M. Cash, IXICO, London, United Kingdom; H. Crawford, N. C. Fox, S. Gregory, G. Owen, N. Z. Hobbs, N. Lahiri, I. Malone, J. Read, M. J. Say, D. Whitehead, E. Wild, University College London, London, United Kingdom; C. Frost, R. Jones, London School of Hygiene and Tropical Medicine, London, United Kingdom; E. Axelson, H. J. Johnson, D. Langbehn, University of Iowa, IA, United States; and S. Queller, C. Campbell, Indiana University, IN, United States.

Note: Ensure that all images are in NifTI format. Conversion to NifTI is not covered here.
1. Segmentation via SPM 8: Unified Segment
NOTE: This procedure is performed via the SPM8 GUI which operates within Matlab. The SPM8 guide provides further detail and can be found at: http://www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf.
<ol>
	<li>Make sure that SPM8 is installed and set in the software path.</li>
	<li>SPM segmentation is performed using a GUI. To o...

<ol>
	<li>Katuwal, G. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inter-Method+Discrepancies+in+Brain+Volume+Estimation+May+Drive+Inconsistent+Findings+in+Autism.">Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism.</a> Frontiers in Neuroscience. 10, 439 (2016).</li><li>Johnson, E. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recommendations+for+the+Use+of+Automated+Gray+Matter+Segmentation+Tools:+Evidence+from+Huntington's+disease.">Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's disease.</a> Frontiers in Neurology. 8, 519 (2017).</li><li>Schwarz, C. 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S., Ramprashad, A., Thompson, C., Botti, J. A., Coman, I. L., Kates, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+FreeSurfer-generated+data+with+and+without+manual+intervention.">A comparison of FreeSurfer-generated data with and without manual intervention.</a> Frontiers in Neuroscience. 9, (2015).</li><li>Tohka, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Partial+volume+effect+modeling+for+segmentation+and+tissue+classification+of+brain+magnetic+resonance+images:+A+review.">Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review.</a> World Journal of Radiology. 6 (11), 855-864 (2014).</li><li>Sled, J. G., Zijdenbos, A. P., Evans, A. 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I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+I.+Segmentation+and+surface+reconstruction.">Cortical surface-based analysis. I. Segmentation and surface reconstruction.</a> NeuroImage. 9, 179-194 (1999).</li><li>Fischl, B., Sereno, M. I., Dale, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortical+surface-based+analysis.+II:+Inflation,+flattening,+and+a+surface-based+coordinate+system.">Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system.</a> NeuroImage. 9, 195-207 (1999).</li><li>Avants, B. B., Tustison, N. J., Wu, J., Cook, P. A., Gee, J. 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The authors have nothing to disclose.

Recently, research has demonstrated that the use of different volumetric methods may have important implications for neuroimaging studies1,2. By publishing protocols that help guide novice users in how to apply different neuroimaging tools, as well as how to perform QC on the results output by these tools, researchers may select the best method to apply to their dataset.
While most steps in this SOP can be adjusted to suit the data and...

Neuroimaging is widely used in both clinical and research settings. There is a current move to improve the reproducibility of studies that quantify brain volume from magnetic resonance imaging (MRI) scans; thus, it is important that investigators share experiences of using available MRI tools for segmenting MRI scans into regional volumes, to improve the standardization and optimization of methods1. This protocol provides a step-by-step guide to using seven different tools to segment the cortical grey matter (CGM; grey matter which excludes subcortical regions) from T1-weighted MRI scans. These tools were previously used in a methodological comparison of segmentation methods2, which demonstrated variable performance between tools on an Huntington&#39;s disease cohort. Since performance of these tools is thought to vary among different datasets, it is important for researchers to test a number of tools before selecting only one to apply to their dataset.
Grey matter (GM) volume is regularly used as a measure of brain morphology. Volumetric measures are generally reliable and able to discriminate between healthy controls and clinical groups3. The volume of different tissue types of brain regions is most often calculated using automated software tools that identify these tissue types. Thus, to create high quality delineations (segmentations) of the GM, accurate delineation of the white matter (WM) and cerebrospinal fluid (CSF) is critical in achieving accuracy of the GM region. There are a number of automated tools that may be used for performing GM segmentation, and each requires different processing steps and results in a different output. A number of studies have applied the tools to different datasets to compare them with one other, and some have optimized specific tools1,4,5,6,7,8,9,10,11. Previous work has demonstrated that variability between volumetric tools can result in inconsistencies within the literature when studying brain volume, and these differences have been suggested as driving factors for false conclusions being drawn about neurological conditions1.
Recently, a comparison of different segmentation tools in a cohort that included both healthy control participants and participants with Huntington&#39;s disease was performed. Huntington&#39;s disease is a genetic neurodegenerative disease with a typical onset in adulthood. Gradual atrophy of subcortical and CGM is a prominent and well-studied neuropathological feature of the disease. The results demonstrated variable performance of seven segmentation tools that were applied to the cohort, supporting previous work that demonstrated variability in findings depending on the software used to calculate brain volumes from MRI scans. This protocol provides information on the processing used in Johnson et al. (2017)2 that encourages careful methodological selection of the most appropriate tools for use in neuroimaging. This manual covers the segmentation of GM volume but does not cover the segmentation of lesions, such as those seen in multiple sclerosis.

automated segmentation of cortical grey matter from t1-weighted mri images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

Average brain volumes for 20 control participants, along with demographic information, is shown in Table 1. This acts as a guide for expected values when using these tools. Results should be viewed in the context of the original T1.nii image. All GM regions should be inspected as per the steps described in section 8. When performing visual QC, it is important to directly compare the GM regions to the T1 scan by viewing them overlaid on the T1.
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Watch this Scientific Journal Video about Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images at JoVE.com

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to ...

This method can answer key questions in the neuroimaging and neurology fields. Such as, whether there are group differences in cortical volume for non-clinical versus clinical populations. The main advantage to this technique is that it enables researchers to use the best tool for their data.To begin, first open the SPM software by opening a MATLAB command window and typing SPM into the command line. Then, to perform unified segmentation, select PET VBM to open the structural MRI toolbox. Now, open the batch editor to perform segmentation on multiple scans at once.Select SPM, Spatial, and Segment, then Data, select Files, and choose the T1-Weighted scans as Input. Next, click on Output Files, Grey Matter, and ensure that Native Space is selected, and repeat this for White Matter. If the CSF segmentation is not required, leave this set to None.If the scans have already been Bias Corrected, change this option to Don't Save Corrected. Then use Clean up any partitions and test all three options prior to running the full analysis. Now, leave the other settings set to defaults and click on the green flag to run the segmentation.The MATLAB window will say Done when segmentation is finished. Finally, perform Visual Quality Control on the resulting Grey Matter NIfTI file. To perform the New Segment option in SPM 8, first select PET VBM before opening the batch editor.Then select SPM, Tools, New Segment, and select the NIfTI format T1 image files to be used. Set the Native Tissue Type option to Native Space, and turn off Tissue Classes that are not required. Also turn off Warped Tissue, then click on the green flag to run the segmentation and perform Visual Quality Control when completed.To perform segmentation in SPM 12 again press PET VBM and open the batch editor. Then select SPM, Spatial Segment, and Data Volumes. Then select Native Space Tissue Type and turn off unneeded Tissue Classes.Set Warped Tissue to None, and click the green flag. Once the segmentation has completed, again be sure to perform Visual Quality Control as detailed in the next section of this protocol. Visual Quality Control can be performed using FSLeyes.Begin by opening a terminal window, then open FSLeyes by typing FSLeyes in the terminal. Then select File, Add from File, and select the original T1 and the segmented regions to view them. Once FSLeyes opens, use the opacity toggle to allow visualization of the underlying T1 image.Also change the color of the segmentation overlay as needed via the color dropdown tab in the top pane. Now, scroll through every slice in the brain and check each one for regions of under or overestimation in the region being inspected. Visual Quality Control is an essential step for this procedure.By comparing your segmented regions to the original T1 scan, you can ensure that your regions are of a high quality, and that your conclusions are biologically accurate. To perform Visual Quality Control of FreeSurfer data using Freeview, open a terminal window and change the directory to the subject folder that contains the processed FreeSurfer output. Then type the command seen on on the screen here to view the volumetric grey matter region overlaid on the T1.Again, scroll through every slice in the brain and check for regions of under or overestimation for the brain region being inspected.Here, we see an example of a failed segmentation displayed on a T1 scan. This segmentation should be reprocessed and excluded from analysis if it cannot be improved. This figure shows examples of the performance of different tools on the temporal lobe on a T1 scan.Examples of a good regional delineation are seen here, while examples of poor regional delineation, showing spillage in the left and right temporal lobes, are shown here. This figure shows examples of the performance of different tools on the occipital lobe on a T1 scan. Here we see the T1 scan with an example of a good regional delineation, while here is an example of a poor regional delineation, showing spillage into the medial dura.Here we see an example of a grey matter region spilled into the dura, highlighted by the blue region. This figure shows an example of a grey matter region that has excluded regions of the cortex from segmentation, best shown in the axial view. While attempting this procedure, it's important to remember to test different tools in your data, and to perform Visual Quality Control on the process scans.After it's development, this technique paved the way for neuroimaging researchers to study changes in brain volume over time without requiring invasive tests.

automated-segmentation-cortical-grey-matter-from-t1-weighted-mri

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JoVE Journal

Neuroscience

8.7K visualizações.  UCL Institute of Neurology. Este método pode responder a perguntas-chave nos campos de neuroimagem e neurologia. Assim como se há diferenças de grupo no volume cortical para populações não clínicas versus clínicas. A principal vantagem dessa técnica é que permite aos pesquisadores usar a melhor ferramenta para seus dados.Para começar, abra primeiro o software SPM abrindo uma janela de comando MATLAB e digitando SPM na linha de comando. Em seguida, para realizar a segmentação unificada, selecione PET ...