A work-efficient GPU algorithm for level set segmentation
ACM SIGGRAPH 2010 Posters
A work-efficient GPU algorithm for level set segmentation
Proceedings of the Conference on High Performance Graphics
Neural process reconstruction from sparse user scribbles
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Interactive large-scale image editing using operator reduction
SIGGRAPH Asia 2011 Posters
Embodied interaction with complex neuronal data in mixed-reality
Proceedings of the 2012 Virtual Reality International Conference
Improving the visualization of electron-microscopy data through optical flow interpolation
Proceedings of the 27th Spring Conference on Computer Graphics
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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.