Flywheel-Connect 3D Slicer Extension

Modern scientific workflows require the utilization of a diverse (and sometimes disparate) set of tools to turn data into insight. With each of these tools designed to excel at a specific set of tasks, shepherding results between tools can require significant effort and expense. Fortunately, Flywheel is an integrated framework facilitating ease-of-use of these tools and the transfer of data between them. The Flywheel-connect tool described here is an 3d Slicer extension which further enhances the Flywheel platform's integrated functions. 

Extending the Flywheel-Connect 3D Slicer Extension

In this article we demonstrate the capacity of Flywheel to extend the utility of 3D Slicer. 3D Slicer is an open source software platform for medical image informatics, image processing, and three-dimensional visualization. While 3D Slicer excels at desktop visualization and local image analysis, it lacks the capacity to orchestrate cloud-centric computing and institutional data resources -- the specific domain in which the Flywheel 3D Slicer extension shines. 

We have leveraged 3D Slicer’s Extension architecture and Flywheel’s Python SDK to provide 3D Slicer access to NifTI images stored within a remote Flywheel instance running on Google Cloud Platform. Called “flywheel-connect”, this 3D Slicer extension is a proof-of-concept utility for downloading and displaying multiple NifTI images from selected acquisitions within a specific Flywheel instance. The “flywheel-connect” extension makes it possible for a 3D Slicer extension user to directly access and load data stored within any Flywheel instance they have access to.


We demonstrate flywheel-connect in the video below using data from the 2017 MICCAI Multimodal Brain Tumor Segmentation Challenge. Firstly, a user-generated api-key from a Flywheel instance is input.  Then, after the “Connect Flywheel” button is pressed, combo-boxes are populated with all of the groups, projects, sessions, and acquisitions that the user has permissions to access. To load an image into the 3D Slicer extension, press “Retrieve Acquisition”. By default, images are cached in a user-specific directory for later use. These directories and images can be deleted directly off of the filesystem or flushed with the “Cache Images” checkbox.

When the data is in 3D Slicer, Slicer-specific operations can be performed on these data. As portrayed in the video, the discrete-valued volume representing tumor segmentation across imaging modalities (FLAIR, T1W, T1CE, T2W) is converted into a label map.  In turn, this label map is converted to a three-dimensional representation with Slicer-provided tools.

Although flywheel-connect demonstrates a usable solution, additional features would greatly increase overall utility for Slicer-centric imaging workflows. First, the capacity to view the results of specific Flywheel analyses. This would, for example, allow the visual inspection of image registration to an atlas or template done in Flywheel. Secondly, saving an entire 3D Slicer workspace in a Flywheel instance.  Along with instantiating this workspace from a Flywheel instance, this feature would provide functionality entirely lacking in 3D Slicer: Management of multiple workspaces relating to the same project.

To try out flywheel-connect for yourself, see the github page for this project. Suggestions and proposed improvements are welcome.

Building Blocks of Imaging AI Use Case: Flywheel delivers presentation at BioData World West 2019

Oct., 10-11, 2019, Hilton San Diego Resort and Spa, San Diego, California– Flywheel Exchange, is sponsoring a demonstration in booth 14, and an imaging AI use case presentation “Scaling Medical Imaging and Machine Learning in Clinical Research: Data Management, Curation, Computational Workflows” at Bio-Data West 2019. To arrange for a private meeting onsite connect with us here!

The Imaging AI use case presentation by Flywheel CEO, Travis Richardson, describes a framework for a scientific workflow which manages imaging data.  The presentation by Richardson addresses creating standardization of imaging data and metadata from multiple, disparate data repositories. 

Specifically, the presentation walks through managing historical clinical trial imaging data sets, located in a variety of repositories, including: vendor neutral archives (VNAs), picture archiving and communication systems (PACS), file servers, cloud servers, thumbdrives, or even DVDs. Moreover, Richardson reviews the Imaging AI use case challenge of bulk upload ingest of imaging data and metadata, as well as automated validation of an organization’s unique DICOM imaging data and metadata files, structures, and formatting. Also, Richardson reviews how the ingested, standardized, and validated imaging data and metadata is then searchable enabling easy construction of new data sets and training models for future imaging AI training models and research projects. 

Imaging AI use case: compliance, automation and reproducibility

Key to the case study is the Flywheel imaging infrastructure platform. Flywheel exchange provides a collaborative workflow, compliant with the requirements of Institutional Review Boards (IRBs), the Health Information Privacy and Portability Act (HIPPA) and General Data Privacy Regulation (GDPR). Flywheel also automates reproducibility as required for funding by the National Institute of Health (NIH).   

The presentation is of interest to scientific workflow researchers, principal investigators (PIs), imaging lab and center directors, and life science teams seeking to avoid IT bottlenecks, improve the efficiency and speed of imaging AI and research scientific discovery. 

BioData West 2019, as an expo which includes biomedical imaging, data, clinical and research professionals as well as AI & big data, alongside start-ups, growth firms, and Fortune 100 life science organizations, the presentation at BioData West 2019 will facilitate conversations across disciplines.

Flywheel Exchange team members are looking forward to hearing about projects and initiatives, as well as to share their recent insights into biomedical imaging , infrastructure, and solving unique imaging AI challenges.

Flywheel's Neuroinformatics Platform: Translating scientific findings into clinical applications


Lerma-Usabiaga led a group from Stanford University and the University of California, San Francisco (UCSF) in developing a framework for translating magnetic resonance imaging (MRI) scientific findings into clinical practice.  Their system is based on the Flywheel neuroinformatics platform, including both the data and computational management tools. The methods framework explores replication, which is essential for valid science, and generalization, which is essential for clinical applications. 

The authors gathered nine data sets into the Flywheel neuroinformatics platform, grouping  them into three categories. Variations in data characteristics begin during acquisition, the authors note, due to calibration differences in MRI instruments between competing MRI vendors and other factors.

Neuroinformatics platform reproducibility

Lerma-Usabiaga, et al., (2019) use Flywheel to support their goal of computational reproducibility. They  note that computational reproducibility is supported by using open source containerized methods whose inputs, outputs, parameters and versioning (provenance) are  stored in the Flywheel neuroinformatics platform. Other scientists can reproduce the analysis by accessing the Flywheel system, if they are authorized by the Institutional Review Board (IRB).

The containers execute the largest and most complex jobs, while  Flywheel’s software development kit (SDK) facilitates data preparation, further statistical analysis, and visualization  (2019, p. 4). Script reproducibility is supported via storage and versioning in a GitHub repository ( while input data and the executed version is stored in the Flywheel neuroinformatics platform.

Neuroinformatics platform for scientific reproducibility

The specific application the authors explore is a potential  biomarker in the white matter that can be used to assess individual subjects, following the  “Precision Medicine” approach emerging in neuroimaging. The authors point to a need for quantitative and objective method frameworks addressing biomarker measurement validation which provides precision, as well as replication, and reproducibility. 

The paper addresses increasing variations in the clinical setting - neuroimaging instruments, populations, and measurement protocols – as well as the problem of algorithmic complexity. Lerma-Usabiaga, et al., (2019) point out that by using containers and storing the analysis history  the Flywheel neuroinformatics platform system implemented “closely align” (2019, p. 8) with the Poldrack et al., (2017) description of scientific reproducibility research tools

“…entire analysis workflow…completely automated in a workflow engine and packaged in a software container or virtual machine to ensure computational reproducibility.” (p. 124, 2017)

Finally, Lerma-Usabiaga, et al., 2019 note that the Flywheel platform is  extensibile, simplifying the analysis of new datasets using identical computational methods and measuring how compliance ranges fluctuate. A  process of continuous data aggregation should allow continuing improvement of the methods and better definition of the compliance range, helping translate scientific research from the lab to the clinic.


Lerma-Usabiaga, G., et al., 2019. Replication and generalization in applied neuroimaging. NeuroImage, a Journal of Brain Function. Volume 202, Article 116048, November 15, 2019.

Marcus, D., et al., 2011. Informatics and data mining tools and strategies for the human connectome project. Front. Neuroinf., 5 (2011), p. 4.

Poldrack, R. A., et al., 2017. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115-126.