National Alliance for Medical Image Computing

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The National Alliance for Medical Image Computing, NA-MIC, is a network of peers. The overall goals of NA-MIC are multi-layered:

  • creating a medical image computing platform
  • performing research on novel image analysis algorithms.
  • deploying these capabilities to enable biomedical research

Interdisciplinary Team

The National Alliance for Medical Imaging Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the center is to provide the infrastructure and environment for the development of computational algorithms and open source technologies, and then oversee the training and dissemination of these tools to the medical research community. This world-class software and development environment serves as a foundation for accelerating the development and deployment of computational tools that are readily accessible to the medical research community. The team combines cutting-edge computer vision research (to create medical imaging analysis algorithms) with state of the art software engineering techniques (based on "extreme" programming techniques in a distributed, open-source environment) to enable computational examination of basic neurosience, neurological disorders, and prostate cancer. In developing this infrastructure resource, the team has significantly expanded upon proven open systems technology and platforms.

Diseases Under Study

The driving biological projects for NA-MIC are schizophrenia, lupus, autism, velocardiofacial syndrome (VCFS) and prostate cancer but the underlying methods and infrastructure are designed to be applicable to many other diseases. The computational tools and open systems technologies and platforms developed by NA-MIC are being used to study anatomical structures and connectivity patterns in the brain, derangements of which have long been thought to play a role in the etiology of schizophrenia. The overall analysis occurred across a range of modalities including diffusion MRI, quantitative EGG, and metabolic and receptor PET, and also included microscopic, genomic, and other image data. It applies to image data from individual patients, and to studies executed across large populations.

Mathematical Models

Mathematical models are the foundation of biomedical computing. To further our understanding of complex diseases, such as schizophrenia, lupus, autism, velocardiofacial syndrome (VCSF) or Alzheimer’s disease, we need complex models that encompass many factors – models of anatomy, morphology, function, interrelation of elements, as well as changes of each as the disease progresses. Although, clearly, these models will evolve from analysis of anatomical, pathological, and clinical data, such models are limited in scope, unless they also incorporate critical information that can best be derived from medical images. This is particularly true since images now encompass techniques beyond the visible light photograph and microscopic images of biology’s early years. Imaging, today, is better viewed as a collection of geometrically arranged arrays of data samples that measure an infinite range of information. Physical attributes such as tissue type can be derived from traditional imagery, but diverse other physical and physiological properties, such as time-varying hemoglobin deoxygenation due to localized changes in neuronal metabolism, or vector-valued water diffusion through and within tissue, are now also quantifiable with modern imaging techniques. The broadening scope of imaging as a way to organize our observations of the biophysical world has led to a dramatic increase in our ability to apply processing techniques and to combine multiple channels of data to instantiate sophisticated and complex mathematical models of physiological function and dysfunction. We believe that as a National Center for Biomedical Computing, dedicated to the advancement of medical image computing, we are well positioned to have a broad and significant impact on experimental, clinical biomedical, and behavioral research.


It is not enough for image analysis efforts to demonstrate new scientific principles. These efforts must be converted into working systems that are easily used and accessed by scientific practitioners. The National Alliance for Medical Image Computing (NA-MIC) integrates the efforts of leading researchers with a shared vision for development and distribution of the tools required to advance the power of imaging as a methodology for quantifying and analyzing biomedical data. This shared vision is based on a thorough composition of computational methods, from image acquisition to analysis, that builds on the best available practices in algorithm development, software engineering, and application of medical image computing for understanding and mitigating the effects of disease and disability.


NA-MIC’s goal is to develop, integrate, and deploy computational image analysis systems that are applicable to multiple diseases, in different organs. To provide focus for these efforts, a set of key problems in schizophrenia research was selected as the initial Driving Biological Projects (DBPs) for NA-MIC. Schizophrenia is a multi-faceted illness affecting 1% of the US population and consuming a significant portion of the healthcare budget – estimates of yearly costs are $60 billion. Yet the science of schizophrenia is only now beginning to take concrete form, primarily because neuroimaging techniques are finally providing a sufficiently detailed picture of the structure of the living brain and tracking the way the brain functions in controlled experimental settings. These sophisticated images – time-varying, multi-spectral, scalar, and vector-valued – are fruitful ground for computation, because the patient’s anatomy forms a three-dimensional coordinate system in which to accurately combine the multiple sources of information. Thus, in addition to making important contributions to the understanding of schizophrenia as an illness, the richness of this problem domain drove the creation of computational tools and techniques with broad and significant applicability to many important areas of image-based biomedical computing, particularly as we expand the scope of NA-MIC to incorporate new DBPs, both within the brain and in other organs.

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