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Mendeley readers
Attention Score in Context
| Title |
SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, January 2017
|
| DOI | 10.3389/fninf.2017.00002 |
| Pubmed ID | |
| Authors |
Ahmed Serag, Alastair G. Wilkinson, Emma J. Telford, Rozalia Pataky, Sarah A. Sparrow, Devasuda Anblagan, Gillian Macnaught, Scott I. Semple, James P. Boardman |
| Abstract |
Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38-42 weeks gestational age), children and adolescents (4-17 years) and adults (35-71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course. |
X Demographics
The data shown below were collected from the profiles of 8 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 | 2 | 25% |
| Spain | 1 | 13% |
| Ireland | 1 | 13% |
| Switzerland | 1 | 13% |
| Qatar | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 2 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 2% |
| Unknown | 46 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 21% |
| Researcher | 9 | 19% |
| Professor | 5 | 11% |
| Student > Doctoral Student | 3 | 6% |
| Student > Master | 3 | 6% |
| Other | 7 | 15% |
| Unknown | 10 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 8 | 17% |
| Engineering | 7 | 15% |
| Computer Science | 5 | 11% |
| Medicine and Dentistry | 5 | 11% |
| Psychology | 4 | 9% |
| Other | 3 | 6% |
| Unknown | 15 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 30 November 2017.
All research outputs
#6,522,393
of 24,318,236 outputs
Outputs from Frontiers in Neuroinformatics
#302
of 798 outputs
Outputs of similar age
#115,980
of 425,085 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#8
of 17 outputs
Altmetric has tracked 24,318,236 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 798 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 61% 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 425,085 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 72% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.