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
Liu, Sidong; Cai, Weidong; Pujol, Sonia et al. (2016) Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci 8:23
Kapur, Tina; Pieper, Steve; Fedorov, Andriy et al. (2016) Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience. Med Image Anal 33:176-80
Luo, Xinchao; Zhu, Lixing; Zhu, Hongtu (2016) Single-index varying coefficient model for functional responses. Biometrics 72:1275-1284
Roth, Robert M; Garlinghouse, Matthew A; Flashman, Laura A et al. (2016) Apathy Is Associated With Ventral Striatum Volume in Schizophrenia Spectrum Disorder. J Neuropsychiatry Clin Neurosci 28:191-4
Herrick, Rick; Horton, William; Olsen, Timothy et al. (2016) XNAT Central: Open sourcing imaging research data. Neuroimage 124:1093-6
Sadeghi, Neda; Gilmore, John H; Gerig, Guido (2016) Twin-singleton developmental study of brain white matter anatomy. Hum Brain Mapp :
Wang, Bo; Prastawa, Marcel; Irimia, Andrei et al. (2016) Modeling 4D Pathological Changes by Leveraging Normative Models. Comput Vis Image Underst 151:3-13
Li, Bolun; Shi, Jie; Gutman, Boris A et al. (2016) Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 11:e0152901
Ruellas, Antonio Carlos de Oliveira; Yatabe, Marilia Sayako; Souki, Bernardo Quiroga et al. (2016) 3D Mandibular Superimposition: Comparison of Regions of Reference for Voxel-Based Registration. PLoS One 11:e0157625
Oguz, Ipek; Cates, Josh; Datar, Manasi et al. (2016) Entropy-based particle correspondence for shape populations. Int J Comput Assist Radiol Surg 11:1221-32

Showing the most recent 10 out of 625 publications