Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Springer International Publishing
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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
|
|---|---|
| Book title |
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
|
| Published in |
arXiv, September 2021
|
| DOI | 10.1007/978-3-030-87193-2_16 |
| Book ISBNs |
978-3-03-087192-5, 978-3-03-087193-2
|
| Authors |
Zudi Lin, Donglai Wei, Mariela D. Petkova, Yuelong Wu, Zergham Ahmed, Krishna Swaroop K, Silin Zou, Nils Wendt, Jonathan Boulanger-Weill, Xueying Wang, Nagaraju Dhanyasi, Ignacio Arganda-Carreras, Florian Engert, Jeff Lichtman, Hanspeter Pfister, Lin, Zudi, Wei, Donglai, Petkova, Mariela D., Wu, Yuelong, Ahmed, Zergham, K, Krishna Swaroop, Zou, Silin, Wendt, Nils, Boulanger-Weill, Jonathan, Wang, Xueying, Dhanyasi, Nagaraju, Arganda-Carreras, Ignacio, Engert, Florian, Lichtman, Jeff, Pfister, Hanspeter |
X Demographics
The data shown below were collected from the profiles of 5 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 Kingdom | 1 | 20% |
| Netherlands | 1 | 20% |
| Japan | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 18% |
| Student > Doctoral Student | 2 | 12% |
| Researcher | 2 | 12% |
| Student > Ph. D. Student | 1 | 6% |
| Librarian | 1 | 6% |
| Other | 2 | 12% |
| Unknown | 6 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 18% |
| Agricultural and Biological Sciences | 3 | 18% |
| Engineering | 3 | 18% |
| Neuroscience | 2 | 12% |
| Unknown | 6 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 09 December 2021.
All research outputs
#14,095,539
of 24,093,053 outputs
Outputs from arXiv
#219,358
of 1,018,817 outputs
Outputs of similar age
#187,055
of 420,874 outputs
Outputs of similar age from arXiv
#7,441
of 34,558 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,018,817 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 77% 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 420,874 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 34,558 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.