Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience

- Traditional academic reward system and mechanisms for research funding create “valley of death” for research prototypes by valuing novelty over robustness and reproducibility.
- The US-based National Alliance for Medical Image Computing (NA-MIC) was formed in 2004 to address this problem by creating robust tools that enable reproducible science.
- NA-MIC transformed 3D Slicer, a research prototype for medical image processing, into a professionally maintained, robust, and extensible software platform for translational research that is downloaded over a thousand times a week by researchers worldwide.
- A key ingredient in the success of NA-MIC is the strong sense of belonging to the community and a shared ownership of outcomes.  This has been fostered through a semi-annual “NA-MIC Project Week” hackathon series that has run continuously since 2005.

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science (“Open Science”); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

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