Limits...
Pydpiper: a flexible toolkit for constructing novel registration pipelines.

Friedel M, van Eede MC, Pipitone J, Chakravarty MM, Lerch JP - Front Neuroinform (2014)

Bottom Line: Pydpiper offers five key innovations.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.

View Article: PubMed Central - PubMed

Affiliation: Mouse Imaging Centre, Hospital for Sick Children Toronto, ON, Canada.

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.

No MeSH data available.


Related in: MedlinePlus

Schematic of the two-level pipeline.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4115634&req=5

Figure 11: Schematic of the two-level pipeline.

Mentions: Two-level registration is a registration paradigm that creates both subject and population averages. It is appropriate for data sets where all subjects are scanned multiple times, but in contrast to the types of longitudinal registration described in section 4.3, all timepoints for a given subject can be registered together. This is done using iterative group-wise registration to create a subject-specific average, enabling meaningful statistical comparison among all timepoints for a given subject. All of these subject-specific averages are then registered together, again using the iterative group-wise procedure, to create a population average. Transform concatenation can then be used to calculate the appropriate transform from the population average to each subject specific average, and subsequently to each individual scan. This allows for inter-subject comparison at each of the timepoints in the study. A schematic of this is shown in Figure 11.


Pydpiper: a flexible toolkit for constructing novel registration pipelines.

Friedel M, van Eede MC, Pipitone J, Chakravarty MM, Lerch JP - Front Neuroinform (2014)

Schematic of the two-level pipeline.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4115634&req=5

Figure 11: Schematic of the two-level pipeline.
Mentions: Two-level registration is a registration paradigm that creates both subject and population averages. It is appropriate for data sets where all subjects are scanned multiple times, but in contrast to the types of longitudinal registration described in section 4.3, all timepoints for a given subject can be registered together. This is done using iterative group-wise registration to create a subject-specific average, enabling meaningful statistical comparison among all timepoints for a given subject. All of these subject-specific averages are then registered together, again using the iterative group-wise procedure, to create a population average. Transform concatenation can then be used to calculate the appropriate transform from the population average to each subject specific average, and subsequently to each individual scan. This allows for inter-subject comparison at each of the timepoints in the study. A schematic of this is shown in Figure 11.

Bottom Line: Pydpiper offers five key innovations.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.

View Article: PubMed Central - PubMed

Affiliation: Mouse Imaging Centre, Hospital for Sick Children Toronto, ON, Canada.

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.

No MeSH data available.


Related in: MedlinePlus