Scalable and interactive segmentation and visualization of neural processes in EM datasets.
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.
Jeong
Won-Ki
WK
School of Engineering and Applied Sciences, Harvard University, USA. wkjeong@seas.harvard.edu
Beyer
Johanna
J
Hadwiger
Markus
M
Vazquez
Amelio
A
Pfister
Hanspeter
H
Whitaker
Ross T
RT
D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000033:anatomy & histology; D016009:Chi-Square Distribution; D003196:Computer Graphics; D016208:Databases, Factual; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008854:Microscopy, Electron / Q000379:methods*; D009415:Nerve Net / Q000033:anatomy & histology*; D016011:Normal Distribution