Overview
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The overall goals of NA-MIC are multifaceted:
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| The National Alliance for Medical Image Computing, NA-MIC, is a network of peers, with funded activities at multiple centers in the U.S. and multiple international collaborations. In the above schematic, each disease process, anatomic location, funding institution, and geographic location has a corresponding color code. Colon cancer, for example, is identified by a yellow dot. Six of these projects are funded by NIH institutes (MH, RR, EB, HL, CA, NS). One additional project is funded by the NSF. |
A MULTI-INSTITUTIONAL, interdisciplinary team of computer scientists, software engineers, and medical investigators, NA-MIC develops computational tools for the analysis and visualization of medical image data. The purpose of the Center is to provide infrastructure and a suitable environment for the development of computational algorithms and open-source technologies. The Center also oversees 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 needed to solve pressing biological problems. The team combines cutting-edge computer vision research (to create medical image 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, with an emphasis on basic neurosience, neurological disorders including schizophrenia and Alzheimer's disease, and more recently prostate cancer. In developing this infrastructure resource, the team has significantly expanded upon proven open systems technologies and platforms.
The current driving biological projects (DBPs) for NA-MIC are schizophrenia, lupus, autism, velocardiofacial syndrome (VCFS) and prostate cancer. The underlying methods and infrastructure, however, are 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 in brain anatomy have long been thought to play a role in the etiology of schizophrenia. The overall analysis encompasses a range of modalities including diffusion MRI, quantitative EGG, and metabolic and receptor PET, as well as microscopic, genomic, and other image data. The algorithms derived from this effort can be applied to image data from individual patients as well as large study populations.
Mathematical models are the foundation of biomedical computing. To further our understanding of complex diseases, such as schizophrenia and Alzheimer's disease, lupus, autism, and velocardiofacial syndrome (VCSF), we need complex models that encompass many factors – models of anatomy, morphology, function, interrelation of elements, as well as changes that take place over time as the disease progresses. Although models can be developed from the analysis of anatomical, pathological, and clinical data, such models are limited in scope unless they incorporate critical information 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 with modern imaging techniques, many other diverse 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, can be quantified. The broadening scope of imaging as a tool to organize observations on the biophysical world has led to a dramatic increase in processing techniques and ontologies that combine multiple channels of data to instantiate sophisticated and complex mathematical models of physiological function and dysfunction. As a National Center for Biomedical Computing dedicated to the advancement of medical image computing, NA-MIC is well positioned to have a broad and significant impact on experimental, clinical biomedical, and behavioral research.
It is no longer sufficient for image analysis efforts to demonstrate new scientific principles. These efforts must be converted into working software systems that are easily used and accessed by scientific practitioners. NA-MIC integrates the efforts of leading researchers to advance the power of imaging as a methodology for quantifying and analyzing biomedical data. These researchers have a shared vision for the development and distribution of tools required to analyze biomedical data. This shared vision is based on a thorough composition of computational methods, from image acquisition to analysis, that build 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 and multiple 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 that affects 1% of the US population and consumes 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 has driven the creation of computational tools and techniques with broad and significant applicability to many important areas of image-based biomedical computing. These methods continue to support the expansion of NA-MIC's scope as it incorporates new DBPs, both within the brain and in other organs.
Examples of the potential for computational image analysis are shown in Figure 1. Figure 1a illustrates the rich detail that can be extracted and visualized using the tools this project provides. Figure 1b demonstrates a morphologic comparison of ventricles for selected comparison populations. Figure 1c demonstrates a visualization of cortical anisotropy. Figure 1d demonstrates an analysis of shape difference in the hippocampus between normals and subjects with schizophrenia. These examples clearly illustrate the potential power of image analysis tools to provide insight into disease effects.