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

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6 Author(s)
Won-Ki Jeong ; School of Engineering and Applied Sciences at Harvard University ; Johanna Beyer ; Markus Hadwiger ; Amelio Vazquez
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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.

Published in:

IEEE Transactions on Visualization and Computer Graphics  (Volume:15 ,  Issue: 6 )