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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, January 2020
|
| DOI | 10.3389/fninf.2019.00077 |
| Pubmed ID | |
| Authors |
Shengyu Fan, Yueyan Bian, Hao Chen, Yan Kang, Qi Yang, Tao Tan |
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 % |
|---|---|---|
| Netherlands | 1 | 25% |
| United States | 1 | 25% |
| Switzerland | 1 | 25% |
| Canada | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 15% |
| Researcher | 4 | 12% |
| Student > Ph. D. Student | 3 | 9% |
| Lecturer | 2 | 6% |
| Student > Bachelor | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 15 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 6 | 18% |
| Computer Science | 3 | 9% |
| Neuroscience | 2 | 6% |
| Nursing and Health Professions | 1 | 3% |
| Physics and Astronomy | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 17 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 February 2020.
All research outputs
#15,484,645
of 24,998,746 outputs
Outputs from Frontiers in Neuroinformatics
#502
of 813 outputs
Outputs of similar age
#251,669
of 469,778 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#11
of 16 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 813 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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 469,778 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.