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IEEE Trans Vis Comput Graph. 2009 Nov-Dec;15(6):1505-14. doi: 10.1109/TVCG.2009.178.

Scalable and interactive segmentation and visualization of neural processes in EM datasets.

Author information

  • 1School of Engineering and Applied Sciences, Harvard University, USA. wkjeong@seas.harvard.edu

Abstract

Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuro-scientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes.

PMID:
19834227
[PubMed - indexed for MEDLINE]
PMCID:
PMC3179915
Free PMC Article
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