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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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VICE: Visual Identification and Correction of Neural Circuit Errors
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework’s utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
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- Award ID(s):
- 1835231
- PAR ID:
- 10312219
- Date Published:
- Journal Name:
- Eurographics Conference on Visualization (EuroVis)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1111/cgf.14320
