Abstract.

In the framework of this paper, modern tools with an open-source code that can be used to automate the task of MR morphometry are investigated. The formats of storage and processing of MRI images used in modern software are considered. The key principles and algorithms of these programs and libraries are described. The analysis of the scientific literature related to the evaluation of the quality of work of this software and their comparative characteristics is given. Based on the analysis, we
propose a scheme of a hardware-software complex that could be used to automate the processes of post-processor MRI data processing, storage and analysis of processing data, and volumetric analyzes. For volumetric analysis, the FMRIB Software Library software is mainly used, with the possibility of combining it with the Statistical Parametric Mapping software for converting MRI image formats and improving segmentation quality. The result of the prototype of this complex was visualized.

Keywords:

alzheimer's disease, volumetric characteristics of the hippocampus, magnetic resonance volumetry, post-processing MRI data processing, free software.

PP. 70-86.

DOI 10.14357/20718632190407

References

1. Jahno N.N., Gridin V.N., Perepelov V.A., Merkulova M.S., Perepelova E.M., Sinicyn V.E. [Magnetic resonance imaging in the diagnosis of Alzheimer's disease at the stage of moderate cognitive impairment] // Informacionnye tehnologii i matematicheskoe modelirovanie sistem 2017. Trudy mezhdunarodnoj nauchnotehnicheskoj konferencii.[Information technology and mathematical modeling of systems 2017. Proceedings of the international scientific and technical conference.] – Moscow.: Federal'noe gosudarstvennoe bjudzhetnoe uchrezhdenie nauki Centr informacionnyh tehnologij v proektirovanii Rossijskoj akademii nauk, 2017. – 136-141. (In Russ.).
2. Anan'eva N. I., Zaluckaja N. M., Neznanov N. G., Salomatina T. A., Ahmerova L. R., Andreev E. V., Stulov I. K. [The role of neuroimaging of the hippocampus in the early diagnosis of Alzheimer's disease] // Luchevaja diagnostika i terapija — Radiation Diagnostics and Therapy, 2019, № 1(10), p. 18-19 (In Russ.).
3. Gridin V.N., Jahno N.N., Sinicyn V.E., Perepelov V.A., Trufanov M.I., Vinogradov V.A. [Hippocampus search algorithm on a series of magnetic resonance images of the brain in the diagnosis of Alzheimer's] // Informacionnye tehnologii i vychislitel'nye sistemy — Information Technology and Computing Systems. 2018. № 4. p. 23-32. (In Russ.).
4. Sinicyn V.E., Gridin V.N., Perepelova E.M., Perepelov V.A. [The role of methods for the quantitative analysis of magnetic resonance imaging data in the diagnosis of early Alzheimer's disease] // Vestnik rentgenologii i radiologii — Journal of radiology and nuclear medicine. 2017. Volume. 98. № 5. p. 269-274. (In Russ.)
5. Bidgood WD Jr, Horii SC, Prior FW, Van Syckle DE. Understanding and using DICOM, the data interchange standard for biomedical imaging. J Am Med Inform Assoc. 1997; 4(3):199–212. doi:10.1136/jamia.1997.0040199
6. Neuroimaging Informatics Technology Initiative / NIfTI-1 Data Format. Available at: https://www.nitrc.org/docman/view.php/26/204/TheNIfTI1Format2004.pdf. (accessed: 26.10.2019).
7. Research and Education at Mayo Clinic / ANALYZETM 7.5 File Format Available at: https://rportal.mayo.edu/bir/ANALYZE75.pdf. (accessed: 26.10.2019).
8. Talairach J., Szikla G. Atlas of stereotactic concepts to the surgery of epilepsy. 1967
9. Kalmady, Sunil & Venkatasubramanian, Ganesan & Shivakumar, Venkataram & Gautham, S & Subramaniam, Aditi & Jose, Dania & Maitra, Arindam & Ravi, Vasanthapuram & Gangadhar, Bn. (2014). Relationship between Interleukin-6 Gene Polymorphism and Hippocampal Volume in Antipsychotic-Naïve Schizophrenia: Evidence for Differential Susceptibility. PloS one. 9. e96021. 10.1371/journal.pone.0096021.
10. Dale A.M., Fishl B., Sereno M.I. Cortical surface-based analysis I: Segmentation and surface reconstruction. Neuroimage, 1999, no. 9, pp. 179-194.
11. Fischl B., Sereno M.I., Dale A.M. Cortical surface-based analysis II: Inflation, flattening, and a surface-based coordinate system. Neuroimage, 1999, no. 9, pp. 195-207.
12. Fischl B., van der Kouwe A., Destrieux C. et al. Automatically parcellating the human cerebral cortex. Cerebral Cortex, 2004, no. 14, pp. 11-22.
13. Fischl B., Salat D., Busa E. et al. Whole brain segmentation. Automated labeling of neuroanatomical structures in the human brain. Neuron, 2002, no. 33, vol. 3, pp. 341-355.
14. Bartos, A., Gregus, D., Ibrahim, I., Tintěra, J. Brain volumes and their ratios in Alzheimer´s disease on magnetic resonance imaging segmented using Freesurfer 6.0 // Psychiatry Research: Neuroimaging, 2019, Volume 287, pp. 70-74. https://doi.org/287.10.1016/j.pscychresns.2019.01.014.
15. Magonov E. P., Kataeva G. V., Trofimova T. N. [Modern methods for automatically calculating the volume of intracranial space in MRI-morphometry of the brain] Vestnik NovGU — Bulletin of NovSU. 2015. №2 (85). (In Russ.).
16. FreeSurfer HippocampalSegmentation, February 20, 2018. URL:https://donghwa-kim.github.io/hippocampalsegmentation.html (accessed: 26.10.2019).
17. Ashburner J, Friston KJ. Unified segmentation. NeuroImage. 2005; 26:839-51. doi: 10.1016/j.neuroimage.2005.02.018. PubMed PMID: 15955494.
18. M. Batyrbekova, T. Prell, B. Stubendorff, R. Steinbach, M. Bokemeyer, T. Mayer, V. Hartung, O.W. Witte, J. Grosskreutz. P48. Progression of cerebellar involvement in amyotrophic lateral sclerosis as seen by SUIT/ CAT12 voxel-based morphometry and D50 disease modelling // Clinical Neurophysiology, 2018, Volume 129, Issue 8, pp 86-87.
19. C. Gaser, R. Dahnke. CAT - A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. HBM 2016. http://www.neuro.unijena.de/hbm2016/GaserHBM2016.pdf
20. S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002.
21. Ivana Išgum, Manon J.N.L. Benders, Brian Avants, M. Jorge Cardoso, Serena J. Counsell, Elda Fischi Gomez, Laura Gui, Petra S. Hűppi, Karina J. Kersbergen, Antonios Makropoulos, Andrew Melbourne, Pim Moeskops, Christian P. Mol, Maria Kuklisova-Murgasova, Daniel Rueckert, Julia A. Schnabel, Vedran Srhoj-Egekher, Jue Wu, Siying Wang, Linda S. de Vries, Max A. Viergever, Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge// Medical Image Analysis, 2015, vol. 20, Issue 1, pp 131-151.
22. Patenaude, B., Smith, S.M., Kennedy, D., and Jenkinson M. A Bayesian Model of Shape and Appearance for Subcortical Brain NeuroImage, 56(3):907-922, 2011.
23. Jenkinson, M., Bannister, P., Brady, J. M. and Smith, S. M. Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841, 2002.
24. Greve, D.N. and Fischl, B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1):63-72, 2009.
25. Brett, M., Johnsrude, I. S., Owen, A. M. The problem of functional localization in the human brain // Nature Reviews Neuroscience volume 3, pages243–249 (2002).
26. Zubov A.Ju., Senjukova O.V. [Segmentation of images of magnetic resonance imaging of the brain using comparison with several atlases]. GRAFIKON 2015 Trudy Jubilejnoj 25-j Mezhdunarodnoj nauchnoj konferencii [GRAPHICON 2015 Proceedings of the 25th International Scientific Conference]. 2015. p. 56-61. (In Russ.).
27. Brian Patenaude. Bayesian Statistical Models of Shape and Appearance for Subcortical Brain Segmentation. Oxford Centre for Functional Magnetic Resonance Imaging of the Brain Department of Clinical Neurology University of Oxford Trinity Term, 2007.
28. Watson C, Andermann F, Gloor P, Jones-Gotman M, Peters T, Evans A, Olivier A, Melanson D, Leroux G. Anatomic basis of amygdaloid and hippocampal volume measurement by magnetic resonance imaging. Neurology 1992; 42(9):1743–1750. [PubMed: 1513464]
29. Pantel, J.; Cretsinger, K.; Keefe, H. Hippocmapus tracing guidelines. 1998.
30. Emma R. Mulder, Remko A. de Jong, Dirk L. Knol, Ronald A. van Schijndel, Keith S. Cover, Pieter J. Visser, Frederik Barkhof, Hugo Vrenken. Hippocampal volume change measurement: Quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST // NeuroImage, Volume 92, 2014, P. 169-181, https://doi.org/10.1016/j.neuroimage.2014.01.058.
31. Schoemaker, Dorothee & Buss, Claudia & Head, Kevin & Sandman, Curt & Davis, Elysia & Chakravarty, M. & Gauthier, Serge & Pruessner, Jens. (2016). Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation. NeuroImage. 129. 10.1016/j.neuroimage.2016.01.038.
32. Akudjedu, Theophilus & Nabulsi, Leila & Makelyte, Migle & Scanlon, Cathy & Hehir, Sarah & Casey, Helen & Ambati, Srinath & Kenney, Joanne & Odonoghue, Stefani & McDermott, Emma & Kilmartin, Liam & Dockery, Peter & Mcdonald, Colm & Hallahan, Brian & Cannon, Dara. (2018). A comparative study of segmentation techniques for the quantification of brain subcortical volume. Brain Imaging and Behavior. 10.1007/s11682-018-9835-y.
33. Keith S. Cover, Ronald A. van Schijndel, Adriaan Versteeg, Kelvin K. Leung, Emma R. Mulder, Remko A. Jong, Peter J. Visser, Alberto Redolfi, Jerome Revillard, Baptiste Grenier, David Manset, Soheil Damangir, Paolo Bosco, Hugo Vrenken, Bob W. van Dijk, Giovanni B. Frisoni, Frederik Barkhof. Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease // Psychiatry Research: Neuroimaging, Volume 252, 2016, p. 26-35 https://doi.org/10.1016/j.pscychresns.2016.04.006.
34. Cover, Keith & Schijndel, Ronald & Bosco, Paolo & Damangir, Soheil & Redolfi, Alberto. (2018). Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years? Psychiatry Research: Neuroimaging. 280. 10.1016/j.pscychresns.2018.06.011.
35. Doring, T. M., Kubo, T. T., Cruz, L. C., Juruena, M. F., Fainberg, J. , Domingues, R. C. and Gasparetto, E. L. (2011), Evaluation of hippocampal volume based on MR imaging in patients with bipolar affective disorder applying manual and automatic segmentation techniques. J. Magn. Reson. Imaging, 33: 565-572. doi:10.1002/jmri.22473.
36. Nugent, A. C., Luckenbaugh, D. A., Wood, S. E., Bogers, W. , Zarate, C. A. and Drevets, W. C. (2013), Automated subcortical segmentation using FIRST: Test–retest reliability, interscanner reliability, and comparison to manual segmentation. Hum. Brain Mapp, 34: 2313-2329. doi:10.1002/hbm.22068.
37. Grimm O, Pohlack S, Cacciaglia R, Winkelmann T, Plichta MM, Demirakca T, Flor H. Amygdalar and hippocampal volume: A comparison between manual segmentation, Freesurfer and VBM // J Neurosci Methods. 2015 Sep 30; 253:254-61. doi: 10.1016/j.jneumeth.2015.05.024. Epub 2015 Jun 6.
38. Perlaki, G., Horvath, R., Nagy, S. A., Bogner, P., Doczi, T., Janszky, J., & Orsi, G. (2017). Comparison of accuracy between FSL's FIRST and Freesurfer for caudate nucleus and putamen segmentation. Scientific reports, 7(1), 2418. doi:10.1038/s41598-017-02584-5.
39. Kazemi, K., Noorizadeh, N. (2014). Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation. Journal of biomedical physics & engineering, 4(1), 13–26.
40. Palumbo, L. et al. Evaluation of the intra- and intermethod agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer // Physica Medica: European Journal of Medical Physics, 2019, Volume 64, pp. 261 – 272
41. Wenger, E., Mårtensson, J., Noack, H., Bodammer, N. C., Kühn, S., Schaefer, S., Heinze, H., Düzel, E., Bäckman, L., Lindenberger, U. and Lövdén, M. (2014), Comparing manual and automatic segmentation of hippocampal volumes: Reliability and validity issues in younger and older brains. Hum. Brain Mapp., 35: 4236-4248. doi:10.1002/hbm.22473.
42. Schmidt, M. F., Storrs, J. M., Freeman, K. B., Jack, C. R., Jr, Turner, S. T., Griswold, M. E., & Mosley, T. H., Jr (2018). A comparison of manual tracing and FreeSurfer for estimating hippocampal volume over the adult lifespan. Human brain mapping, 39(6), 2500–2513. doi:10.1002/hbm.24017.
43. Bartel, F., Visser, Martin, de Ruiter, Michiel, Belderbos, José, Barkhof, F., Vrenken, H., De Munck, Jan, Herk, M. (2019). Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia. NeuroImage: Clinical. 23. 101902. 10.1016/j.nicl.2019.101902.
44. Vanderson Dill, Pedro Costa Klein, Alexandre Rosa Franco, Márcio Sarroglia Pinho. Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters // Computers in Biology and Medicine, 2018, Volume 95, pp. 90-98, https://doi.org/10.1016/j.compbiomed.2018.02.005.
45. Bartel, F., Vrenken, H., van Herk, M., de Ruiter, M., Belderbos, J., Hulshof, J., & de Munck, J. C. (2019). FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method. PloS one, 14(1), e0210641. doi:10.1371/journal.pone.0210641.
46. Visser E, Keuken MC, Douaud G, Gaura V, Bachoud-Levi AC, Remy P, Forstmann BU, Jenkinson M (2016). Automatic segmentation of the striatum and globus pallidus using MIST: Multimodal Image Segmentation Tool. NeuroImage 125:479-497.
47. Xiang Feng, Andreas Deistung, Michael G. Dwyer, Jesper Hagemeier, Paul Polak, Jessica Lebenberg, Frédérique Frouin, Robert Zivadinov, Jürgen R. Reichenbach, Ferdinand Schweser. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM) // Magnetic Resonance Imaging, 2017, Volume 39, pp. 110-122, https://doi.org/10.1016/j.mri.2017.02.002.
48. Michael Amann, Michaela Andělová, Armanda Pfister, Nicole Mueller-Lenke, Stefan Traud, Julia Reinhardt, Stefano Magon, Kerstin Bendfeldt, Ludwig Kappos, Ernst-Wilhelm Radue, Christoph Stippich, Till Sprenger. Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST) // NeuroImage: Clinical, 2015, Volume 7, pp. 43-52, https://doi.org/10.1016/j.nicl.2014.11.010.
49. Muschelli, John et al. “fslr: Connecting the FSL Software-with R.” The R journal vol. 7,1 (2015): 163-175.
50. Oficial'nyj sajt Nacional'noj biblioteki im. N. Je. Baumana [The official website of the National Library. N.E. Bauman] /Couchbase Server Available at: https://ru.bmstu.wiki/Couchbase_Server. (accessed: 26.10.2019).