This title appears in the Scientific Report :
2016
Please use the identifier:
http://dx.doi.org/10.1007/978-3-319-50862-7_3 in citations.
Towards Large-Scale Fiber Orientation Models of the Brain – Automation and Parallelization of a Seeded Region Growing Segmentation of High-Resolution Brain Section Images
Towards Large-Scale Fiber Orientation Models of the Brain – Automation and Parallelization of a Seeded Region Growing Segmentation of High-Resolution Brain Section Images
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...
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Personal Name(s): | Lührs, Anna (Corresponding author) |
---|---|
Bücker, Oliver / Axer, Markus | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 Jülich Supercomputing Center; JSC |
Published in: |
Brain-Inspired Computing |
Imprint: |
Cham
Springer International Publishing
2016
|
Physical Description: |
28 - 42 |
ISBN: |
978-3-319-50861-0 (print) 978-3-319-50862-7 (electronic) |
DOI: |
10.1007/978-3-319-50862-7_3 |
Conference: | International Workshop on Brain-Inspired Computing, Cetraro (Italy), 2015-07-06 - 2015-07-10 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
SimLab Neuroscience The Human Brain Project Supercomputing and Modelling for the Human Brain Theory, modelling and simulation Computational Science and Mathematical Methods |
Series Title: |
Lecture Notes in Computer Science
10087 |
Publikationsportal JuSER |
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. |