Monthly Archives: January 2016

1.22.2016 – Charles Guttmann: A Virtual Laboratory for Image-based Neuroscience Discovery: Collaborative Research and Science Education in the Era of “big data”

Speaker: Charles Guttmann is the founding Director of the Center for Neurological Imaging (CNI) at Brigham and Women’s Hospital and an Associate Professor of Radiology at Harvard Medical School. His team applies quantitative neuroimaging strategies to the study of neurological diseases, such as multiple sclerosis (MS) and cerebra-vascular diseases in the elderly. Dr. Guttmann has also spearheaded the development of informatics infrastructure in support of large-scale neuroimaging discovery research, including an image-centered, multi-disciplinary database and image analysis workflow management system, as well as – more recently – a virtual laboratory for collaborative neuroscience research.

Talk Summary: Digital medical imaging has become ubiquitous throughout the world. Every day, millions of patients are imaged for many clinical reasons, using modalities such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), digital mammography and digital X-ray systems. Many of these modalities acquire detailed anatomical, structural, and functional data useful for diagnosing, treating, or monitoring diseases in individual patients. These vast amounts of data are archived around the world together with clinical and biological information, and represent an invaluable asset for advancing our collective understanding of human biology and for discovering, developing, and validating therapies to treat disease.

While new approaches are now being developed and deployed to increase the utilization of large data sets (e.g., sophisticated technologies to mine ‘big data’), these advances largely ignore core biomedical image data. A single MRI or CT scan of the human brain may yield thousands of complex images; such images have proven resistant to automated analytical methods due to limited accuracy and the need for manual review and correction. As a result, interpreting complex biomedical images remains a labor-intensive task for human experts (radiologists and other physicians or scientists). So while our enormous and growing archives of biomedical image data play roles in the care of individual patients (e.g., prior film comparisons to assess changes), this resource has been barely touched for more ambitious purposes.

We have developed a web-based “virtual laboratory” that integrates project-specific data federation, easy user-friendly project design and accelerated execution of complex experiments, as well as intuitive and interactive data analysis.

In addition, since the automated components of image analysis are no longer a limiting factor, we propose attacking this problem from a different angle. Modern information systems have facilitated new ways of mobilizing groups of people with common interests, including ‘crowdsourcing’ strategies to address complex tasks. Our proposed “virtual laboratory” will enable rapid execution of interactive image analysis, by intimately involving laypeople through a well-honed combination of education and crowdsourcing. This virtual laboratory, tentatively called SPINE (Structured Planning and Implementation of New Explorations), can be seen as an advanced variant of crowdsourcing with far-reaching educational and research potential.

In its first implementation, SPINE will be a virtual laboratory designed to accelerate scientific discovery, while educating and actively involving professionals, as well as laypeople in neurological research. Underlying SPINE’s “crowdsourcing” vision is the recognition that expert review and correction tasks can be broken down into manageable sub-tasks that can be standardized for a professional audience, and easily and rapidly learned even by laypeople. SPINE’s educational, training, and testing elements, delivered through web-based systems that incorporate social networking, gaming components, and motivational technologies, allow laypeople from childhood to advanced age to learn and be certified as experts within highly specific domains. These self-selected ‘citizen scientists’ will master the skills and knowledge needed to analyze biomedical images while they contribute meaningfully to important real-world imaging based research.

One goal of SPINE is to create a worldwide community of experts capable of rapidly, affordably, and reliably analyzing large image datasets. SPINE’s vision, however, reaches far beyond crowdsourcing. It aims to revolutionize and expand the scientific community and the way real science can be performed.

A second crucial element of SPINE is the “living experiment”. At SPINE’s core are specific modules that combine automated steps (performed by computer algorithms) and interactive “crowd-sourced” expert steps (performed by our citizen scientist community) into unique workflows yielding specific measurements. The SPINE modules are the modern equivalent of the methods section of a scientific article. Using modern communication technologies (video, web) allows for a much richer and more accurate illustration of the steps taken to achieve a particular measurement. These modules also include both training and testing elements to certify the participants. Further, validation datasets are utilized to guarantee a new level of methods standardization between laboratories across the world. Most importantly, these modules will enable new levels of transparency, openness, and collaboration in scientific explorations, allowing for unprecedented constructive scrutiny of imaging experiments. The recyclability of these modules for reproducing and extending previous scientific experiments, as well as for asking novel questions with new, original research is a key aspect of our vision.

A further differentiating aspect of SPINE is our emphasis of educational aspects. Among our primary goals, we aim to entice young people to learn science, “one skill at a time”. Young and old laypeople will be guided towards finding the right balance between breadth and depth of learning, by setting appropriate educational and productivity targets in the system.
SPINE aims to accelerate research by intimately meshing research and education, enabling and empowering lay-people to contribute in unprecedented ways, and creating projects open to scrutiny and expansion.