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Pydpiper: a flexible toolkit for constructing novel registration pipelines

Overview of attention for article published in Frontiers in Neuroinformatics, July 2014
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Title
Pydpiper: a flexible toolkit for constructing novel registration pipelines
Published in
Frontiers in Neuroinformatics, July 2014
DOI 10.3389/fninf.2014.00067
Pubmed ID
Authors

Miriam Friedel, Matthijs C. van Eede, Jon Pipitone, M. Mallar Chakravarty, Jason P. Lerch

Abstract

Using neuroimaging technologies to elucidate the relationship between genotype and phenotype and brain and behavior will be a key contribution to biomedical research in the twenty-first century. Among the many methods for analyzing neuroimaging data, image registration deserves particular attention due to its wide range of applications. Finding strategies to register together many images and analyze the differences between them can be a challenge, particularly given that different experimental designs require different registration strategies. Moreover, writing software that can handle different types of image registration pipelines in a flexible, reusable and extensible way can be challenging. In response to this challenge, we have created Pydpiper, a neuroimaging registration toolkit written in Python. Pydpiper is an open-source, freely available software package that provides multiple modules for various image registration applications. Pydpiper offers five key innovations. Specifically: (1) a robust file handling class that allows access to outputs from all stages of registration at any point in the pipeline; (2) the ability of the framework to eliminate duplicate stages; (3) reusable, easy to subclass modules; (4) a development toolkit written for non-developers; (5) four complete applications that run complex image registration pipelines "out-of-the-box." In this paper, we will discuss both the general Pydpiper framework and the various ways in which component modules can be pieced together to easily create new registration pipelines. This will include a discussion of the core principles motivating code development and a comparison of Pydpiper with other available toolkits. We also provide a comprehensive, line-by-line example to orient users with limited programming knowledge and highlight some of the most useful features of Pydpiper. In addition, we will present the four current applications of the code.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 1%
Unknown 88 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 17%
Researcher 15 17%
Student > Master 14 16%
Student > Bachelor 10 11%
Other 9 10%
Other 11 12%
Unknown 15 17%
Readers by discipline Count As %
Neuroscience 21 24%
Agricultural and Biological Sciences 18 20%
Biochemistry, Genetics and Molecular Biology 8 9%
Medicine and Dentistry 8 9%
Physics and Astronomy 4 4%
Other 9 10%
Unknown 21 24%
Attention Score in Context

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 14 March 2016.
All research outputs
#14,198,374
of 22,759,618 outputs
Outputs from Frontiers in Neuroinformatics
#482
of 743 outputs
Outputs of similar age
#118,002
of 228,677 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#9
of 12 outputs
Altmetric has tracked 22,759,618 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 743 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 31st percentile – i.e., 31% 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 228,677 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 12 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.