The objective of the Computer Science Core is to develop a national computing infrastructure for image analysis to be used in biomedical research and leading-edge clinical research and practice. To meet this broad objective, the Computer Science Core is organized as two scientific teams: an Algorithms team and an Engineering team. The Algorithms team develops new techniques for image analysis to address the most pressing challenges posed by clinical researchers. The Engineering team develops software applications, delivers computational platforms, and establishes software engineering practices for algorithm researchers and for clinical hypothesis formation and testing. The combined efforts of Algorithms and Engineering produce the NA-MIC Kit, an open source platform for medical image computing that includes an end-user application (the 3D Slicer), image analysis algorithms and workflows distributed as plug-ins and reusable libraries, a PACS-like image and data management platform, computational platforms for data streaming and distributed computing, and software engineering and software quality methods and tools. The two teams bring complementary skills to the technical challenges in NA-MIC. The Algorithms group is led by five senior investigators from four academic institutions. Their combined background provides renowned expertise in variational, statistical, and geometrical approaches to image analysis. The Engineering group is led by five senior investigators from two small businesses, one industrial research facility, and two academic institutions. Their combined background spans visualization, medical image analysis, information systems, scientific computing, and software engineering. Thus, the Computer Science Core of NA-MIC is uniquely positioned with the breadth and depth to deliver a national infrastructure for medical image computing. To drive the development ofthe national infrastructure for medical image computing, this renewal has selected four DBPs that focus on the analysis of images for the understanding of disease, healing processes and adaptations, and curative and palliative therapies. These clinical applications-atrial fibrillation, Huntington's disease, head and neck cancer, traumatic brain injury-emphasize the study of an individual's pathology or injury and how that pathology or injury changes over time. Image analysis in this context requires new methods and tools for image segmentation, registration, statistical analysis, and visualization. Segmentation must be objective and robust while providing efficient interactive editing. Registration must be computationally efficient, but explicltiy accommodate longitudinal data and the nonrigid or nonsmooth nature of injuries and pathology. Characterization of change needs to be succinct and yet provide statistically quantifiable results for multidimensional analysis of structure and function. The software tools to support research in these clinical applications must be flexible enough to accommodate new methods yet enforce software engineering practices to meet the performance and stability demands of clinical settings. The sections that follow describe the Algorithms and Engineering efforts in more detail, including the motivation and aims, the background and context, and the methods, with plans for collaboration between these groups and the project as a whole. Preliminary results are presented both in the methods section below as well as in the Progress Report (Section 2.4) of the proposal, as proscribed by the RFA.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54EB005149-09
Application #
8501015
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
9
Fiscal Year
2013
Total Cost
$1,779,956
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Wachinger, Christian; Brennan, Matthew; Sharp, Greg C et al. (2017) Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means. IEEE Trans Biomed Eng 64:1492-1502
Norton, Isaiah; Essayed, Walid Ibn; Zhang, Fan et al. (2017) SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research. Cancer Res 77:e101-e103
Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2017) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp 38:1009-1024
Veni, Gopalkrishna; Elhabian, Shireen Y; Whitaker, Ross T (2017) ShapeCut: Bayesian surface estimation using shape-driven graph. Med Image Anal 40:11-29
Irimia, Andrei; Goh, Sheng-Yang Matthew; Wade, Adam C et al. (2017) Traumatic Brain Injury Severity, Neuropathophysiology, and Clinical Outcome: Insights from Multimodal Neuroimaging. Front Neurol 8:530
Ghayoor, Ali; Vaidya, Jatin G; Johnson, Hans J (2017) Robust automated constellation-based landmark detection in human brain imaging. Neuroimage :
Swanson, Meghan R; Wolff, Jason J; Elison, Jed T et al. (2017) Splenium development and early spoken language in human infants. Dev Sci 20:
Hong, Sungmin; Fishbaugh, James; Rezanejad, Morteza et al. (2017) Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis. Proc SPIE Int Soc Opt Eng 10133:
Wolff, Jason J; Swanson, Meghan R; Elison, Jed T et al. (2017) Neural circuitry at age 6 months associated with later repetitive behavior and sensory responsiveness in autism. Mol Autism 8:8
Hazlett, Heather Cody; Gu, Hongbin; Munsell, Brent C et al. (2017) Early brain development in infants at high risk for autism spectrum disorder. Nature 542:348-351

Showing the most recent 10 out of 652 publications