| Title |
Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts
|
|---|---|
| Published in |
Journal of Digital Imaging, July 2018
|
| DOI | 10.1007/s10278-018-0108-5 |
| Pubmed ID | |
| Authors |
Dominique Duncan, Rachael Garner, Ivan Zrantchev, Tyler Ard, Bradley Newman, Adam Saslow, Emily Wanserski, Arthur W. Toga |
| 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. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 22% |
| Researcher | 7 | 14% |
| Student > Ph. D. Student | 6 | 12% |
| Student > Bachelor | 3 | 6% |
| Other | 2 | 4% |
| Other | 4 | 8% |
| Unknown | 18 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 16% |
| Engineering | 4 | 8% |
| Psychology | 4 | 8% |
| Medicine and Dentistry | 3 | 6% |
| Social Sciences | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 22 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 November 2019.
All research outputs
#2,227,556
of 24,578,676 outputs
Outputs from Journal of Digital Imaging
#49
of 1,119 outputs
Outputs of similar age
#45,004
of 333,621 outputs
Outputs of similar age from Journal of Digital Imaging
#2
of 19 outputs
Altmetric has tracked 24,578,676 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,119 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,621 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.