Infrastructure for sharing standardized clinical brain scans across hospitals

Erp, Theo G.M. van; Chervenak, Ann; Kesselman, Carl; D’Arcy, Mike; Sobell, Janet L.; Keator, David B.; Dahm, Lisa; Murry, Jim; Law, Meng; Hasso, Anton N.; Ames, Joseph; Macciardi, Fabìo; Potkin, Steven G.

doi:10.1109/bibmw.2011.6112547

Cited by 4 publications

(2 citation statements)

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“…However, to automate other data curation steps the dataset first needs to follow a structured format, making our tool a crucial first step in the overall pipeline. The increasing data complexity, both in volume and in the number of different types of data, not only shows a need for a proper data curation pipeline, but also shows the need for a standardized data structure for scans and their associated metadata (van Erp et al 2011;Gorgolewski et al 2016;Lambin et al 2017). The widespread adoption of a common, standardized data structure would be favorable over the use of our tool or similar tools.…”

Section: Discussionmentioning

confidence: 99%

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

2020

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With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize precontrast T1-weighted (T1w), post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment, we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer's subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.

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Section: Discussionmentioning

confidence: 99%

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

2020

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“…Meta-data are descriptive elements associated with data that provide additional clarity regarding acquisition parameters, experimental conditions, analysis procedures, and any other formation about the experiment or analyses that helps one understand and use the data. The benefits of neuroimage data sharing was introduced more than a decade ago (Van Horn et al, 2001; Van Horn and Gazzaniga, 2002), several successful data sharing projects exist (Biswal et al, 2010; Weiner et al, 2012; Nooner et al, 2012), and many of the technical, legal, and social issues of data sharing have been discussed (Poline et al, 2012; Milham, 2012; Mennes et al, 2012), but there currently is no standard format nor a set of lightweight tools that allow small laboratories or individual investigators to share imaging and meta-data in a structured way, nor a set of tools that allows for queries across existing databases or data sharing efforts (Poline et al, 2012). These problems are especially acute when attempting to construct large datasets from data available through online repositories, each of which uses different structures of storing meta-data.…”

Section: Introductionmentioning

confidence: 99%

Towards structured sharing of raw and derived neuroimaging data across existing resources

et al. 2013

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Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery.

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Modality Neutral Techniques for Brain Image Understanding

Keator

2012

Lecture Notes in Computer Science

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Infrastructure for sharing standardized clinical brain scans across hospitals

Cited by 4 publications

References 3 publications

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

Towards structured sharing of raw and derived neuroimaging data across existing resources

Modality Neutral Techniques for Brain Image Understanding

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