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      Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts

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          Abstract

          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.

          Electronic supplementary material

          The online version of this article (10.1007/s10278-018-0108-5) contains supplementary material, which is available to authorized users.

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          Author and article information

          Contributors
          (323) 865-1710 , Dominique.Duncan@loni.usc.edu
          Journal
          J Digit Imaging
          J Digit Imaging
          Journal of Digital Imaging
          Springer International Publishing (Cham )
          0897-1889
          1618-727X
          20 July 2018
          February 2019
          : 32
          : 1
          : 97-104
          Affiliations
          [1 ] ISNI 0000 0001 2156 6853, GRID grid.42505.36, Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, , University of Southern California, ; 2025 Zonal Ave., Los Angeles, CA 90033 USA
          [2 ]RareFaction Interactive, 1725 Camino Palmero Street, 410, Los Angeles, CA 90046 USA
          Author information
          http://orcid.org/0000-0002-6154-9262
          Article
          PMC6382628 PMC6382628 6382628 108
          10.1007/s10278-018-0108-5
          6382628
          30030766
          e48e9f6a-55c0-40c7-8d8b-dc5d1b524797
          © Society for Imaging Informatics in Medicine 2018
          History
          Funding
          Funded by: FundRef http://doi.org/10.13039/100000002, National Institutes of Health;
          Award ID: P41-EB015922
          Award ID: U54EB0020406
          Categories
          Article
          Custom metadata
          © Society for Imaging Informatics in Medicine 2019

          Virtual reality,Quality control,Imaging,MRI,Segmentation
          Virtual reality, Quality control, Imaging, MRI, Segmentation

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