Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts.
Segmentation of MRI scans is a critical part of the workflow process before we can further analyze neuroimaging data. Although there are several automatic tools for segmentation, no segmentation software is perfectly accurate, and manual correction by visually inspecting the segmentation errors is r...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 1; pp. 97 - 105 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134830550&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134830550 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2019 vid: 32 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134830550 134830550 134830550 10.1007/s10278-018-0108-5 134830550 ppf: 97 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts. aug: au: Duncan, Dominique Garner, Rachael Zrantchev, Ivan Ard, Tyler Toga, Arthur W. Newman, Bradley Saslow, Adam Wanserski, Emily affil: Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., 90033, Los Angeles, CA, USA sug: subj: Virtual Reality Utilization Magnetic Resonance Imaging Methods Software Diagnosis, Brain Methods Image Processing, Computer Assisted Methods Sensitivity and Specificity Human Quality Control (Technology) ab: Segmentation of MRI scans is a critical part of the workflow process before we can further analyze neuroimaging data. Although there are several automatic tools for segmentation, no segmentation software is perfectly accurate, and manual correction by visually inspecting the segmentation errors is required. The process of correcting these errors is tedious and time-consuming, so we present a novel method of performing this task in a head-mounted virtual reality interactive system with a new software, Virtual Brain Segmenter (VBS). We provide the results of user testing on 30 volunteers to show the benefits of our tool as a more efficient, intuitive, and engaging alternative compared with the current method of correcting segmentation errors. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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