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 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.
KeywordsPolarized light imaging Fiber architecture Human brain Workflow Region growing Segmentation Scaling Supercomputing
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).
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