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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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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.

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This work is supported by the National Institutes of Health grants P41-EB015922, U54EB0020406, and the University of Southern California Provost’s Postdoctoral Scholar Research Grant.

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Correspondence to Dominique Duncan.

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Duncan, D., Garner, R., Zrantchev, I. et al. Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts. J Digit Imaging 32, 97–104 (2019).

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