BioVis 2011 Abstract
Reconstructing Neural Structures from Sparse User Scribbles
We present a novel semi-automatic method for segmenting neural structures in large, highly anisotropic EM (electron microscopy) image stacks. Our method takes advantage of sparse scribble annotations provided by the user to guide a 3D variational segmentation model, thereby allowing our method to globally optimally enforce 3D geometric constraints on the segmentation. Moreover, we leverage a novel algorithm for propagating segmentation constraints through the image stack via optimal volumetric pathways, thereby allowing our method to compute highly accurate 3D segmentations from very sparse user input. We demonstrate that, on average, our method is 68% more accurate than previous semi-automatic methods.
BioVis 2011 Papers and Abstracts
