Medical Collaboration in an Open Source Environment: The Story of the Surgical Planning Lab
The Surgical Planning Lab (SPL) at Brigham and Women's Hospital (BWH) has been at the forefront of continual advancements in medical image computing and translational medicine for almost thirty years. Its success has been intimately tied to the rapid progression of networking, computation, and imaging during that time. For example, the SPL was one of the first labs at BWH to use the internet for imaging research, at one point consuming about 1/3 of the entire hospital's network bandwidth. Much of the lab’s early work was done on an advanced network consisting of Sun graphics workstations and other infrastructure rivaling that available in any computer science university lab.
However, the SPL’s true strength has always come from using this advanced computational technology as a foundation to advance medicine through interdisciplinary collaborations.
Collaborative research fostered by technology
The SPL’s collaborations have included long standing partnerships with computer scientists at MIT, imaging researchers in the BWH Radiology department and at the Martinos Center of Massachusetts General Hospital, and joint projects with members of different hospital departments and visiting researchers from around the world. Out of these collaborations has come a shared pool of tools and knowledge that in turn enable new ideas and new projects. The SPL’s multi-disciplinary staff and other lab visitors help familiarize newer people to the resources through peer-to-peer training. Foundational ideas in the fields of medical image segmentation, registration, visualization, imaging physics, and image guided therapy have emerged from this rich collaborative environment over the years.
3D Slicer
The SPL’s development of the popular 3D Slicer medical visualization and analysis software platform (also known as just “Slicer”) came about in large part because of this collaborative environment. At the time of Slicer’s development, other medical image analysis software was either proprietary or restrictively licenced in a way that discouraged reuse. Slicer, in contrast, was open and free, so visiting scholars and other collaborators could use Slicer-based software tools without any affiliation with SPL or the hospital. Many of those collaborators were able to start up new research programs in their home institutions without rewriting their code.
Over time, Slicer has become more and more critical to a wide range of research projects both inside and outside of Brigham and Women's Hospital. The Slicer development team fully embraced the growing open source software movement. Development shifted from an in-house effort to an international confederation of developers and researchers, including professional programmers at Kitware LLC. NIH funding across multiple grants and contracts was essential for supporting the development of the robust Slicer platform.
The free nature of Slicer produced an interesting dynamic with respect to the projects for which it was used. Researchers could just try Slicer out for their particular application rather than having to justify the cost of expensive proprietary software. This ease of exploration and adoption led to an explosion of new applications for Slicer. Entire specialized ecosystems built up around specific capabilities of Slicer including intraoperative imaging, image-guided intervention, and robotics. Not only did Slicer make the development of image-guided therapy at BWH possible, it opened the door for similar capabilities at other hospitals and research institutions.
The easy availability of Slicer has enabled projects beyond medical imaging. Slicer has been applied to visualizing star-forming regions in the constellation Perseus, understanding the morphology of a wide range of animals, and viewing the river deltas of the Amazon. As new capabilities are added to Slicer in one application domain, a strong philosophy of giving back to the community allows those contributions to be used by other projects and domains.
An open research community
The free and open nature of Slicer and its capabilities in turn enabled the free and open exchange of ideas within its user and developer communities. The Slicer mailing list and Slicer discussion forum have become places where researchers facing similar problems or collaborators with a shared vision could work together. Twice-yearly in-person Slicer project weeks allowed these collaborators to meet in person to accelerate their ideas. The unique combination of capability, availability, and openness of Slicer became the glue binding together a diverse community of developers, researchers, and physicians.
Building international collaborations in Africa and beyond
While Slicer has always had an international user base, several specific efforts have brought it to new countries over the last decade. For instance, a single neurosurgeon who trained at the SPL is responsible for popularizing Slicer across hospitals and medical centers throughout China.
In another remarkable example, a research and training collaboration led by Juan Ruiz Alzola at the University of Gran Canaria Las Palmas is responsible for introducing Slicer into multiple African medical environments through a “train the trainers” program for African physicians. This innovative program brought together experts from the SPL and leading physicians from a wide range of African countries to the Canary Islands to provide training in Slicer and to seed medical collaborations that continue to this day. In Senegal, for example, Slicer has helped physicians explain prostate cancer to patients in a way that breaks down local taboos of prostate examination. In Mauritania, Slicer and the SPL's Open Anatomy project have revolutionized the training of medical students by combining anatomy and radiology teaching through hands-on segmentation of patient data. Students can perform their assignments using Slicer on their own laptops even in countries with limited technology resources. In a country where the major medical school was only established in 2006, dedicated Mauritanian anatomy instructors have constructed a curriculum around Slicer beyond what is available in many first-year anatomy classes anywhere in the West.
Virtual collaboration using Slicer
The global COVID-19 pandemic disrupted collaborative scientific efforts around the world, including those based around Slicer technology. However, the pandemic also drove the worldwide adoption of video conferencing technologies such as Zoom. The combination of video conferencing with the growing ubiquity of Slicer allowed collaborations to continue and later grow as the world emerged from pandemic lockdowns. In fact, the combination of Slicer and video conferencing reduced the necessity of the kinds of in-person meetings that pose significant barriers to international collaborators based on financial or visa-related issues.
As the result of African outreach and the availability of these new communication technologies, the Slicer community has welcomed many more members from countries on the African continent and in other previously underrepresented areas of the world. In turn, researchers in these countries as well as those in Europe and Latin America have banded together to internationalize and translate Slicer's user interface into native languages through a Chan Zuckerberg-funded development effort.
Collaborative open source medical hardware
The open source software revolution that helped fuel Slicer, combined with low-cost fabrication methods such as 3D printing, has produced an analogous revolution in open source hardware design of medical devices such as image-guided therapy systems, ultrasound-driven imaging, and most recently low-cost open source MRI machines. These low-cost community-developed hardware efforts aim to address the shortcomings of expensive proprietary systems deployed in challenging medical environments such as African hospitals. Proprietary systems, which are sometimes donated by major companies, have ongoing support and maintenance costs that are often difficult for developing countries to manage locally. In contrast, open source medical hardware is much better suited for less expensive local support. Systems can be better tailored to the specific needs of a hospital or medical center. Moreover, the local medical team can become part of the larger community using and developing similar devices. In this way, hardware contributes to community in the same way that community encourages the further development of hardware.
Medical communities supported by a combination of open software and open hardware in turn become natural incubators for translational medical breakthroughs that would be impossible through any other mechanism.
Accelerating science using AI
The most recent enabling technology for these collaborative medical research efforts has been the rise of artificial intelligence for both medical image processing and general-purpose use. Artificial intelligence algorithms can segment a patient's medical scans with a comprehensiveness and speed beyond what could have been imagined even five years ago. AI image processing algorithms work hand in hand with open source imaging hardware to produce high-quality images using low-cost systems. Finally, large language models represent a revolution in code development, information retrieval, and data understanding, even for teams of people speaking different human languages. Simply put, AI is accelerating the development efforts of experts and allowing more people to contribute meaningfully to collaborative medical efforts.
A model for collaborative medicine
The humble beginnings of the SPL more than 30 years ago was based on the idea that interdisciplinary teams of smart people can use technology and imaging to solve some of the greatest challenges of human health. That vision flourishes today, assisted by rapid advances in software and hardware but driven by the collaborative efforts of diverse expert communities spanning nations, disciplines, and circumstances.
The SPL's proven model of scientific collaboration is powerful because of its inclusiveness, its low barrier to entry, its model of both peer-based and expert-based training, and its relentless focus on open, team-based development. In a world that continues to face barriers and inequities between institutions, scientific disciplines, and geopolitical boundaries, this model offers a realistic and hopeful path – a path to a future where the unique knowledge and experiences of many individual researchers come together to solve the world’s healthcare challenges.