Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets

Authors

Won-Ki Jeong; Johanna Beyer; Markus Hadwiger; Amelio Vazquez; Hanspeter Pfister; Ross T. Whitaker



Abstract

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.

BibTex entry

@article { 269, title = {Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets}, journal = {IEEE Transactions on Visualization and Computer Graphics }, volume = {15}, year = {2009}, pages = {1505-1514}, author = {Won-Ki Jeong and Johanna Beyer and Markus Hadwiger and Amelio Vazquez and Hanspeter Pfister and Ross T. Whitaker} }