Abstract
To understand the microscopical organization of the human brain including cellular and fiber architectures, it is a necessary prerequisite to build virtual models of the brain on a sound biological basis. 3D Polarized Light Imaging (3D-PLI) provides a window to analyze the fiber architecture and the fibers’ intricate inter-connections at microscopic resolutions. Considering the complexity and the pure size of the human brain with its nearly 86 billion nerve cells, 3D-PLI is challenging with respect to data handling and analysis in the TeraByte to PetaByte ranges, and inevitably requires supercomputing facilities. Parallelization and automation of image processing steps open up new perspectives to speed up the generation of new high resolution models of the human brain to provide groundbreaking insights into the brain’s three-dimensional micro architecture. Here, we will describe the implementation and the performance of a parallelized semi-automated seeded region growing algorithm used to classify tissue and background components in up to one million 3D-PLI images acquired from an entire human brain. This algorithm represents an important element of a complex UNICORE-based analysis workflow ultimately aiming at the extraction of spatial fiber orientations from 3D-PLI measurements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Axer, M., Amunts, K., Gräßel, D., Palm, C., Dammers, J., Axer, H., Pietrzyk, U., Zilles, K.: A novel approach to the human connectome: ultra-high resolution mapping of fiber tracts in the brain. NeuroImage 54, 1091–1101 (2011)
Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)
Amunts, K., Bücker, O., Axer, M.: Towards a multiscale, high-resolution model of the human brain. In: Grandinetti, L., Lippert, T., Petkov, N. (eds.) BrainComp 2013. LNCS, vol. 8603, pp. 3–14. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12084-3_1
Axer, M., Gräßel, D., Kleiner, M., Dammers, J., Dickscheid, T., Reckfort, J., Hütz, T., Eiben, B., Pietrzyk, U., Zilles, K., Amunts, K.: High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging (3D-PLI). Front. Neuroinformatics 5, 34 (2011)
Bader, D.A., Jájá, J., Harwood, D., Davis, L.S.: Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study. J. Supercomputing 10(2), 141–168 (1996)
Grady, L., Schiwietz, T., Aharon, S., Westermann, R.: Random walks for interactive organ segmentation in two and three dimensions: implementation and validation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 773–780. Springer, Heidelberg (2005). doi:10.1007/11566489_95
Happ, P.N., Ferreira, R.S., Bentes, C., Costa, G., Feitosa, R.Q.: Multiresolution segmentation: a parallel approach for high resolution image segmentation in multicore architectures. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-4/C7, 28–32 (2005)
Happ, P.N., Feitosa, R.Q., Bentes, C., Farias, R.: A parallel image segmentation algorithm on GPUs. In: Proceedings of the 4th GEOBIA, pp. 580–585, May 2012
Huang, C.-L.: Parallel image segmentation using modified Hopfield model. Pattern Recogn. Lett. 13(5), 345–353 (1992)
Hagan, A., Zhao, Y.: Parallel 3D Image segmentation of large data sets on a GPU cluster. In: Bebis, G., et al. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 960–969. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10520-3_92
Khotanzad, A., Bouarfa, A.: Image segmentation by a parallel, non-parametric histogram based clustering algorithm. Pattern Recogn. 23(9), 961–973 (1990)
Moga, A.N., Gabbouj, M.: Parallel marker-based image segmentation with watershed transformation. J. Parallel Distrib. Comput. 51(1), 27–45 (1998)
ANL Mathematics and Computer Science. The Message Passing Interface (MPI) standard. http://www.mcs.anl.gov/research/projects/mpi/. Accessed 29 October 2015
NVIDIA. CUDA Parallel Computing. http://www.nvidia.co.uk/object/cuda-parallel-computing-uk.html. Accessed 29 October 2015
Westhoff, A.: Hybrid parallelization of a seeded region growing segmentation of brain images for a GPU cluster. In: ARCS 2014: 27th International Conference on Architecture of Computing Systems - Workshop Proceedings, p. 8. Berlin, Lübeck (Germany), 25 Feb 2014–28 Feb 2014, VDE Verlag, February 2014
Wassenberg, J., Middelmann, W., Sanders, P.: An efficient parallel algorithm for graph-based image segmentation. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1003–1010. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03767-2_122
Zhuge, Y., Cao, Y., Udupa, J.K., Miller, R.W.: Parallel fuzzy connected image segmentation on GPU. Med. Phys. 38(7), 4365–4371 (2011)
Acknowledgement
This work was partially supported by the Helmholtz Association portfolio theme “Supercomputing and Modeling for the Human Brain” and by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Lührs, A., Bücker, O., Axer, M. (2016). Towards Large-Scale Fiber Orientation Models of the Brain – Automation and Parallelization of a Seeded Region Growing Segmentation of High-Resolution Brain Section Images. In: Amunts, K., Grandinetti, L., Lippert, T., Petkov, N. (eds) Brain-Inspired Computing. BrainComp 2015. Lecture Notes in Computer Science(), vol 10087. Springer, Cham. https://doi.org/10.1007/978-3-319-50862-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-50862-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50861-0
Online ISBN: 978-3-319-50862-7
eBook Packages: Computer ScienceComputer Science (R0)