| Title |
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions
|
|---|---|
| Published in |
Journal of Digital Imaging, June 2017
|
| DOI | 10.1007/s10278-017-9983-4 |
| Pubmed ID | |
| Authors |
Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley J. Erickson |
X Demographics
The data shown below were collected from the profiles of 90 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 30 | 33% |
| Spain | 7 | 8% |
| Australia | 3 | 3% |
| France | 3 | 3% |
| Switzerland | 2 | 2% |
| Netherlands | 1 | 1% |
| Denmark | 1 | 1% |
| Hungary | 1 | 1% |
| United Kingdom | 1 | 1% |
| Other | 6 | 7% |
| Unknown | 35 | 39% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 77 | 86% |
| Scientists | 8 | 9% |
| Practitioners (doctors, other healthcare professionals) | 3 | 3% |
| Science communicators (journalists, bloggers, editors) | 2 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,161 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Unknown | 1160 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 195 | 17% |
| Student > Master | 165 | 14% |
| Researcher | 139 | 12% |
| Student > Bachelor | 86 | 7% |
| Student > Doctoral Student | 59 | 5% |
| Other | 139 | 12% |
| Unknown | 378 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 248 | 21% |
| Engineering | 169 | 15% |
| Medicine and Dentistry | 88 | 8% |
| Neuroscience | 78 | 7% |
| Agricultural and Biological Sciences | 31 | 3% |
| Other | 116 | 10% |
| Unknown | 431 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 67. 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 26 March 2024.
All research outputs
#645,569
of 25,837,817 outputs
Outputs from Journal of Digital Imaging
#4
of 1,008 outputs
Outputs of similar age
#13,296
of 333,356 outputs
Outputs of similar age from Journal of Digital Imaging
#2
of 22 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,008 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 99% 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 333,356 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 22 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 90% of its contemporaries.