VICE: Visual Identification and Correction of Neural Circuit Errors

Simple item page
dc.contributor.authorGonda, Felixen_US
dc.contributor.authorWang, Xueyingen_US
dc.contributor.authorBeyer, Johannaen_US
dc.contributor.authorHadwiger, Markusen_US
dc.contributor.authorLichtman, Jeff W.en_US
dc.contributor.authorPfister, Hanspeteren_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:02:37Z
dc.date.available2021-06-12T11:02:37Z
dc.date.issued2021
dc.description.abstractA 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.en_US
dc.description.number3
dc.description.sectionheadersBio-Medical Image Analysis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.14320
dc.identifier.issn1467-8659
dc.identifier.pages447-458
dc.identifier.urihttps://doi.org/10.1111/cgf.14320
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14320
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman centered computing
dc.subjectWeb
dc.subjectbased interaction
dc.subjectScientific visualization
dc.titleVICE: Visual Identification and Correction of Neural Circuit Errorsen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
v40i3pp447-458.pdf
Size:
22.71 MB
Format:
Adobe Portable Document Format
Download
Collections
40-Issue 3