Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Visualization and Computer Graphics
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
Tracking as Segmentation of Spatial-Temporal Volumes by Anisotropic Weighted TV
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets
IEEE Transactions on Visualization and Computer Graphics
Automatic markup of neural cell membranes using boosted decision stumps
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Geometrical consistent 3D tracing of neuronal processes in ssTEM data
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
A fully automated approach to segmentation of irregularly shaped cellular structures in EM images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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We present a novel semi-automatic method for segmenting neural processes 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 evaluate our method by reconstructing 16 neural processes in a 1024×1024×50 nanometer-scale EM image stack of a mouse hippocampus. We demonstrate that, on average, our method is 68% more accurate than previous state-of-the-art semi-automatic methods.