Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts.
Dominique DuncanRachael GarnerIvan ZrantchevTyler ArdBradley NewmanAdam SaslowEmily WanserskiArthur W TogaPublished in: Journal of digital imaging (2020)
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.
Keyphrases
- deep learning
- convolutional neural network
- virtual reality
- patient safety
- magnetic resonance imaging
- contrast enhanced
- adverse drug
- artificial intelligence
- computed tomography
- machine learning
- emergency department
- high resolution
- data analysis
- white matter
- multiple sclerosis
- mass spectrometry
- quality improvement
- blood brain barrier
- big data