Scalable and interactive segmentation and visualization of neural processes in em datasets

Won Ki Jeong*, Johanna Beyer, Markus Hadwiger, Amelio Vazquez, Hanspeter Pfister, Ross T. Whitaker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists 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.

Original languageEnglish (US)
Article number5290767
Pages (from-to)1505-1514
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number6
StatePublished - Nov 2009

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation under Grant No. PHY-0835713, the Austrian Research Promotion Agency FFG, Vienna Science and Technology Fund WWTF, the Harvard Initiative in Innovative Computing (IIC), the National Institutes of Health under Grant No. P41-RR12553-10 and U54-EB005149, and through generous support from Microsoft Research and NVIDIA. We thank our biology collaborators Prof. Jeff Lichtman and Prof. Clay Reid from the Harvard Center for Brain Science for their time and the use of their data. We also wish to thank Dr. Juan C. Tapia, Dr. Ju Lu, Thomas Zhihao Luo, May Zhang, Bo Wang, and Robert Cole Hurley for participating in the user study.


  • Segmentation
  • connectome
  • graphics hardware
  • implicit surface rendering
  • neuroscience
  • volume rendering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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