Pydpiper: a flexible toolkit for constructing novel registration pipelines

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...

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Main Authors:	Friedel, Miriam, van Eede, Matthijs C., Pipitone, Jon, Chakravarty, M. Mallar, Lerch, Jason P.
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4115634/

id	pubmed-4115634
recordtype	oai_dc
spelling	pubmed-41156342014-08-14 Pydpiper: a flexible toolkit for constructing novel registration pipelines Friedel, Miriam van Eede, Matthijs C. Pipitone, Jon Chakravarty, M. Mallar Lerch, Jason P. Neuroscience 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. Frontiers Media S.A. 2014-07-30 /pmc/articles/PMC4115634/ /pubmed/25126069 http://dx.doi.org/10.3389/fninf.2014.00067 Text en Copyright © 2014 Friedel, van Eede, Pipitone, Chakravarty and Lerch. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Friedel, Miriam van Eede, Matthijs C. Pipitone, Jon Chakravarty, M. Mallar Lerch, Jason P.
spellingShingle	Friedel, Miriam van Eede, Matthijs C. Pipitone, Jon Chakravarty, M. Mallar Lerch, Jason P. Pydpiper: a flexible toolkit for constructing novel registration pipelines
author_facet	Friedel, Miriam van Eede, Matthijs C. Pipitone, Jon Chakravarty, M. Mallar Lerch, Jason P.
author_sort	Friedel, Miriam
title	Pydpiper: a flexible toolkit for constructing novel registration pipelines
title_short	Pydpiper: a flexible toolkit for constructing novel registration pipelines
title_full	Pydpiper: a flexible toolkit for constructing novel registration pipelines
title_fullStr	Pydpiper: a flexible toolkit for constructing novel registration pipelines
title_full_unstemmed	Pydpiper: a flexible toolkit for constructing novel registration pipelines
title_sort	pydpiper: a flexible toolkit for constructing novel registration pipelines
description	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.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4115634/
_version_	1613119186414862336

Pydpiper: a flexible toolkit for constructing novel registration pipelines

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