Abstract
The anatomical structure of the thalamus renders its segmentation on 3DT1 images harder due to its low tissue contrast, and not well-defined boundaries. We aimed to investigate the differences in the precision of publicly available segmentation techniques on 3DT1 images acquired at 1.5 T and 3 T machines compared to the thalamic manual segmentation in a pediatric population. Sixty-eight subjects were recruited between the ages of one and 18 years. Manual segmentation of the thalamus was done by three junior raters, and then corrected by an experienced rater. Automated segmentation was then performed with FSL Anat, FIRST, FreeSurfer, MRICloud, and volBrain. A mask of the intersections between the manual and automated segmentation was created for each algorithm to measure the degree of similitude (DICE) with the manual segmentation. The DICE score was shown to be highest using volBrain in all subjects (0.873 ± 0.036), as well as in the 1.5 T (0.871 ± 0.037), and the 3 T (0.875 ± 0.036) groups. FSL-Anat and FIRST came in second and third. MRICloud was shown to have the lowest DICE values. When comparing 1.5 T to 3 T groups, no significant differences were observed in all segmentation methods, except for FIRST (p = 0.038). Age was not a significant predictor of DICE in any of the measurements. When using automated segmentation, the best option in both field strengths would be the use of volBrain. This will achieve results closest to the manual segmentation while reducing the amount of time and computing power needed by researchers.
Similar content being viewed by others
Abbreviations
- BBB:
-
Blood-Brain Barrier
- DICE:
-
DICE Similarity Index
- GM:
-
Gray Matter
- FIRST:
-
FMRIB’s Integrated Registration and Segmentation Tool
- FSL:
-
FMRIB Software Library
- MRI:
-
Magnetic resonance imaging
- WI:
-
Weighted Images
- WM:
-
White Matter
References
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, 46(3), 726–738. https://doi.org/10.1016/j.neuroimage.2009.02.018.
Aubert-Broche, B., Fonov, V., Ghassemi, R., Narayanan, S., Arnold, D. L., Banwell, B., et al. (2011). Regional brain atrophy in children with multiple sclerosis. NeuroImage, 58(2), 409–415. https://doi.org/10.1016/j.neuroimage.2011.03.025.
Azevedo, C. J., Overton, E., Khadka, S., Buckley, J., Liu, S., Sampat, M., et al. (2015). Early CNS neurodegeneration in radiologically isolated syndrome. Neurology(R) neuroimmunology & neuroinflammation, 2(3), e102. https://doi.org/10.1212/NXI.0000000000000102.
Bakshi, R., Dandamudi, V. S. R., Neema, M., De, C., & Bermel, R. a. (2005). Measurement of brain and spinal cord atrophy by magnetic resonance imaging as a tool to monitor multiple sclerosis. Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging, 15(4 Suppl), 30S–45S. https://doi.org/10.1177/1051228405283901.
Bergsland, N., Horakova, D., Dwyer, M. G., Dolezal, O., Seidl, Z. K., Vaneckova, M., et al. (2012). Subcortical and cortical gray matter atrophy in a large sample of patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis. AJNR. American Journal of Neuroradiology, 33(8), 1573–1578. https://doi.org/10.3174/ajnr.A3086.
Courchesne, E., Chisum, H. J., Townsend, J., Cowles, A., Covington, J., Egaas, B., et al. (2000). Normal brain development and aging: Quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology, 216(3), 672–682. https://doi.org/10.1148/radiology.216.3.r00au37672.
Csernansky, J. G., Schindler, M. K., Splinter, N. R., Wang, L., Gado, M., Selemon, L. D., et al. (2004). Abnormalities of thalamic volume and shape in schizophrenia. American Journal of Psychiatry, 161(5), 896–902. https://doi.org/10.1176/appi.ajp.161.5.896.
De Jong, L. W., Van Der Hiele, K., Veer, I. M., Houwing, J. J., Westendorp, R. G. J., Bollen, E. L. E. M., et al. (2008). Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: An MRI study. Brain, 131(12), 3277–3285. https://doi.org/10.1093/brain/awn278.
Duan, Y., Li, X., & Xi, Y. (2007). Thalamus segmentation from diffusion tensor magnetic resonance imaging. International Journal of Biomedical Imaging, 2007, 90216. https://doi.org/10.1155/2007/90216.
Fearing, M. A., Bigler, E. D., Wilde, E. A., Johnson, J. L., Hunter, J. V., Xiaoqi, L., et al. (2008). Morphometric MRI findings in the thalamus and brainstem in children after moderate to severe traumatic brain injury. Journal of Child Neurology, 23(7), 729–737. https://doi.org/10.1177/0883073808314159.
Felten, D. L., Shetty, A. N., & Felten, D. L. (2010). Netter’s atlas of neuroscience. Saunders/Elsevier.
Ganzola, R., Maziade, M., & Duchesne, S. (2014, June). Hippocampus and amygdala volumes in children and young adults at high-risk of schizophrenia: Research synthesis. Schizophrenia Research. https://doi.org/10.1016/j.schres.2014.03.030.
Hannoun, S., Baalbaki, M., Haddad, R., Saaybi, S., El Ayoubi, N. K., Yamout, B. I., et al. (2018). Gadolinium effect on thalamus and whole brain tissue segmentation. Neuroradiology. https://doi.org/10.1007/s00234-018-2082-5.
Jatzko, A., Rothenhöfer, S., Schmitt, A., Gaser, C., Demirakca, T., Weber-Fahr, W., et al. (2006). Hippocampal volume in chronic posttraumatic stress disorder (PTSD): MRI study using two different evaluation methods. Journal of Affective Disorders, 94(1–3), 121–126. https://doi.org/10.1016/j.jad.2006.03.010.
Jovicich, J., Czanner, S., Han, X., Salat, D., van der Kouwe, A., Quinn, B., et al. (2009). MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. NeuroImage, 46(1), 177–192. https://doi.org/10.1016/j.neuroimage.2009.02.010.
Koolschijn, P. C. M. P., Van Haren, N. E. M., Lensvelt-Mulders, G. J. L. M., Hulshoff Pol, H. E., & Kahn, R. S. (2009). Brain volume abnormalities in major depressive disorder: A meta-analysis of magnetic resonance imaging studies. Human Brain Mapping, 30(11), 3719–3735. https://doi.org/10.1002/hbm.20801.
Lee, S. H., Kim, S. S., Tae, W. S., Lee, S. Y., Choi, J. W., Koh, S. B., & Kwon, D. Y. (2011). Regional volume analysis of the Parkinson disease brain in early disease stage: Gray matter, white matter, striatum, and thalamus. American Journal of Neuroradiology, 32(4), 682–687. https://doi.org/10.3174/ajnr.A2372.
Liang, Z. P., & Paul C. Lauterbur. (2000). Principles of magnetic resonance imaging: A signal processingperspective., Wiley-IEEE Press. https://doi.org/10.1109/978047054565.
Mulder, E. R., de Jong, R. A., Knol, D. L., van Schijndel, R. A., Cover, K. S., Visser, P. J., et al. (2014). Hippocampal volume change measurement: Quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST. NeuroImage, 92, 169–181. https://doi.org/10.1016/j.neuroimage.2014.01.058.
Murgasova, M., Dyet, L., Edwards, D., Rutherford, M., Hajnal, J., & Rueckert, D. (2007). Segmentation of brain MRI in young children. Academic Radiology, 14(11), 1350–1366. https://doi.org/10.1016/j.acra.2007.07.020.
Næss-Schmidt, E., Tietze, A., Blicher, J. U., Petersen, M., Mikkelsen, I. K., Coupé, P., et al. (2016). Automatic thalamus and hippocampus segmentation from MP2RAGE: Comparison of publicly available methods and implications for DTI quantification. International Journal of Computer Assisted Radiology and Surgery, 11(11), 1979–1991. https://doi.org/10.1007/s11548-016-1433-0.
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56(3), 907–922. https://doi.org/10.1016/j.neuroimage.2011.02.046.
Radenbach, K., Flaig, V., Schneider-Axmann, T., Usher, J., Reith, W., Falkai, P., et al. (2010). Thalamic volumes in patients with bipolar disorder. European Archives of Psychiatry and Clinical Neuroscience, 260(8), 601–607. https://doi.org/10.1007/s00406-010-0100-7.
Ricci, D., Anker, S., Cowan, F., Pane, M., Gallini, F., Luciano, R., et al. (2006). Thalamic atrophy in infants with PVL and cerebral visual impairment. Early Human Development, 82(9), 591–595. https://doi.org/10.1016/j.earlhumdev.2005.12.007.
Rosenberg, D. R., Benazon, N. R., Gilbert, A., Sullivan, A., & Moore, G. J. (2000). Thalamic volume in pediatric obsessive-compulsive disorder patients before and after cognitive behavioral therapy. Biological psychiatry, 48(4), 294–300. https://doi.org/10.1016/S0006-3223(00)00902-1.
Rotge, J.-Y., Guehl, D., Dilharreguy, B., Tignol, J., Bioulac, B., Allard, M., et al. (2009). Meta-analysis of brain volume changes in obsessive-compulsive disorder. Biological Psychiatry, 65(1), 75–83. https://doi.org/10.1016/j.biopsych.2008.06.019.
Sassi, R. B., Nicoletti, M., Brambilla, P., Mallinger, A. G., Frank, E., Kupfer, D. J., et al. (2002). Increased gray matter volume in lithium-treated bipolar disorder patients. Neuroscience Letters, 329(2), 243–245. https://doi.org/10.1016/S0304-3940(02)00615-8.
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(SUPPL. 1), S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051.
Solomon, A. J., Watts, R., Dewey, B. E., & Reich, D. S. (2017). MRI evaluation of thalamic volume differentiates MS from common mimics. Neurology(R) neuroimmunology & neuroinflammation, 4(5), e387. https://doi.org/10.1212/NXI.0000000000000387.
Tardif, C. L., Collins, D. L., & Pike, G. B. (2010). Regional impact of field strength on voxel-based morphometry results. Human Brain Mapping, 31(7), 943–957. https://doi.org/10.1002/hbm.20908.
Tsatsanis, K. D., Rourke, B. P., Klin, A., Volkmar, F. R., Cicchetti, D., & Schultz, R. T. (2003). Reduced thalamic volume in high-functioning individuals with autism. Biological Psychiatry, 53(2), 121–129. https://doi.org/10.1016/S0006-3223(02)01530-5.
Weisenfeld, N. I., & Warfield, S. K. (2009). Automatic segmentation of newborn brain MRI. NeuroImage, 47(2), 564–572. https://doi.org/10.1016/j.neuroimage.2009.04.068.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hannoun, S., Tutunji, R., El Homsi, M. et al. Automatic Thalamus Segmentation on Unenhanced 3D T1 Weighted Images: Comparison of Publicly Available Segmentation Methods in a Pediatric Population. Neuroinform 17, 443–450 (2019). https://doi.org/10.1007/s12021-018-9408-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-018-9408-7