Abstract
Reconstructed terabyte and petabyte electron microscopy image volumes contain fully-segmented neurons at resolutions fine enough to identify every synaptic connection. After manual or automatic reconstruction, neuroscientists want to extract wiring diagrams and connectivity information to analyze the data at a higher level. Despite significant advances in image acquisition, neuron segmentation, and synapse detection techniques, the extracted wiring diagrams are still quite coarse, and often do not take into account the wealth of information in the densely reconstructed volumes. We propose a synapse-aware skeleton generation strategy to transform the reconstructed volumes into an information-rich yet abstract format on which neuroscientists can perform biological analysis and run simulations. Our method extends existing topological thinning strategies and guarantees a one-to-one correspondence between skeleton endpoints and synapses while simultaneously generating vital geometric statistics on the neuronal processes. We demonstrate our results on three large-scale connectomic datasets and compare against current state-of-the-art skeletonization algorithms.
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Acknowledgements
H. Pfister is supported in part by NSF grant IIS-1607800. We thank Joergen Kornfeld and Winfried Denk’s group for the J0126 data and synapses, and the Connectomics Group at Google led by Viren Jain for the segmentation. For the JWR dataset, we thank Jeff Lichtman’s group at Harvard University for image acquisition, alignment, and ground truth labeling.
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Matejek, B., Wei, D., Wang, X., Zhao, J., Palágyi, K., Pfister, H. (2019). Synapse-Aware Skeleton Generation for Neural Circuits. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_26
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DOI: https://doi.org/10.1007/978-3-030-32239-7_26
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