VICE: Visual Identification and Correction of Neural Circuit Errors
- URL: http://arxiv.org/abs/2105.06861v1
- Date: Fri, 14 May 2021 14:34:58 GMT
- Title: VICE: Visual Identification and Correction of Neural Circuit Errors
- Authors: Felix Gonda, Xueying Wang, Johanna Beyer, Markus Hadwiger, Jeff W.
Lichtman, and Hanspeter Pfister
- Abstract summary: General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level.
This paper presents the design and implementation of an analytics framework that streamlines proofreading.
We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations.
- Score: 24.106813461993085
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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