| Title |
Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI
|
|---|---|
| Published in |
Neuroradiology, August 2019
|
| DOI | 10.1007/s00234-019-02279-w |
| Pubmed ID | |
| Authors |
Alice Le Berre, Koji Kamagata, Yujiro Otsuka, Christina Andica, Taku Hatano, Laetitia Saccenti, Takashi Ogawa, Haruka Takeshige-Amano, Akihiko Wada, Michimasa Suzuki, Akifumi Hagiwara, Ryusuke Irie, Masaaki Hori, Genko Oyama, Yashushi Shimo, Atsushi Umemura, Nobutaka Hattori, Shigeki Aoki |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 14% |
| Student > Bachelor | 7 | 13% |
| Student > Master | 6 | 11% |
| Student > Ph. D. Student | 5 | 9% |
| Student > Doctoral Student | 3 | 5% |
| Other | 7 | 13% |
| Unknown | 20 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 20% |
| Engineering | 8 | 14% |
| Neuroscience | 5 | 9% |
| Computer Science | 4 | 7% |
| Unspecified | 2 | 4% |
| Other | 6 | 11% |
| Unknown | 20 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 13 August 2019.
All research outputs
#18,687,330
of 23,153,849 outputs
Outputs from Neuroradiology
#934
of 1,407 outputs
Outputs of similar age
#256,445
of 344,347 outputs
Outputs of similar age from Neuroradiology
#11
of 33 outputs
Altmetric has tracked 23,153,849 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,407 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 21st percentile – i.e., 21% 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 344,347 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.