Tekne 2019 Award Cloud Computing Finalist — Flywheel's Biomedical Imaging Research Platform

MINNEAPOLIS, Sept. 24, 2019: Flywheel.io, a leading biomedical imaging research platform provider and an essential building block for imaging artificial intelligence (AI) has been acknowledged as a finalist for the Minnesota High Tech Association 2019 Tekne Awards in the Cloud Computing category. For the past two decades, the Tekne Awards have recognized organizations that are leading-edge innovators in science and technology in Minnesota.

Flywheel has been named a finalist in the Tekne 2019 Cloud Computing award category for its Biomedical Imaging Research Platform. The Flywheel platform is a comprehensive scientific workflow for imaging data management, including data capture, curation, computation, and collaboration, all integrated to accelerate imaging research within clinical processes, collaborative multi-site studies, and scalable imaging AI initiatives. 

Flywheel is designed to speed and improve imaging research and scientific discovery; from standardizing and structuring massive volumes of historical imaging data in life sciences for AI and Machine Learning, to reducing time and costs for clinical imaging device scanning (magnetic resonance (MR), positron emission tomography (PET), etc...) and study processes, to testing the functionality of research design, all while ensuring research projects adhere to scientific reproducibility, regulatory compliance, security, and collaboration mandates needed for National Institute of Health (NIH) funding requirements.  

Flywheel’s ease-of-use and scalability make Flywheel.io one of the bioinformatic industry's most respected imaging research workflow platforms with global customers including Fortune 500 life science organizations, AI specialists, and dozens of top-tier NIH-funded imaging research centers at universities like Stanford and Columbia.

"We are honored to be named as a finalist for the Cloud Computing Tekne 2019 Awards. Flywheel is privileged to help principal investigators, data scientists, and imaging center directors build imaging research processes for the future...so they can do more science and less IT in their pursuit of healthcare discoveries," said Flywheel CEO, Travis Richarson.

“This year’s Tekne Award finalists demonstrate thought leadership and are spearheading technology innovation in Minnesota and around the world,” said Jeff Tollefson, President and CEO of the Minnesota High Tech Association. “We look forward to further recognizing these organizations at the 2019 Tekne Awards as well as highlighting the impressive science and technology community here in Minnesota.” 


A full list of Tekne finalists and November 20 gala details are available online at tekneawards.org.  The event emcee is Paul Douglas.

To learn more about the Flywheel - Biomedical Imaging Research Platform, go to https://flywheel.io


@Flywheel_io is honored to be a finalist for the Minnesota High Tech Association @MHTA 2019 Tekne Awards in the Cloud Computing category for its biomedical imaging research platform. Go to https://ctt.ac/peqbs+ to learn more about Flywheel. @Flywheel_IO #MachineLearning #ImagingResearch #Bioinformatics

About Flywheel - Biomedical Imaging Research Platform

The Flywheel - Biomedical Imaging Research Platform is the leading provider of scalable and collaborative imaging research processes in an easy-to-use and scientifically sound platform. Flywheel users are creating the building blocks of AI and machine learning based Precision Medicine in a demanding scientific and regulatory environment with high security, privacy, compliance, reproducibility, scalability, and performance requirements. Founded in 2012, Flywheel customers are using millions of imaging research data in Flywheel platform to transform the future of healthcare and medicine.

About the Minnesota High Tech Association (MHTA)

The Minnesota High Tech Association is an innovation and technology association united in fueling Minnesota's prosperity. We bring together the people of Minnesota's science and technology ecosystem and lead the way in bringing science and technology issues to leaders at Minnesota's Capitol and Washington, D.C. MHTA is the only membership organization that represents Minnesota's entire technology-based economy. Our members include organizations of every size − involved in virtually every aspect of technology creation, production, application and education in Minnesota.

Flywheel.io: Brad Canham | 612-223-7359 | bradcanham@flywheel.io

Minnesota High Tech Association: Claire Ayling | 952-230-4553 | cayling@mhta.org

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 (https://github.com/garikoitz/paper-reproducibility) 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.