National Alliance for Medical Image Computing (NA-MIC) has matured into the national biomedical computing infrastructure for medical image analysis that we envisioned at the start of our first funding cycle six years ago. Through this alliance, we have demonstrated that coordinated innovation in algorithms, software engineering, and outreach processes can enable innovation in biomedical research to address a range of clinical needs. As in the first cycle, Brigham &Women's Hospital (BWH), acting as the prime contractor, has put together a stellar group of experts in both computer science and biomedical sciences consisting of 13 leading institutions across the country and 18 PIs. The Computer Science Core, addressing algorithms and software engineering, work to implement solutions for Core 2 (Driving Biological Projects or DBPs). Core 3 provides technology service. Core 4 provides training and Core 5 provides dissemination of the NA-MIC deliverables. Core 6 (or Admin Core) will coordinate between various Cores, institutions and the science and finance and project management aspects. As per the RFA, Core IA and IB together constitute 50% of our proposed budget and Core 2 constitutes 25%, while Cores 3-6 constitute 25%. All four DBPs address personalized medicine: adapting radiotherapy to accommodate patient changes;guiding cardiac catheters for targeted ablation;assessing consequences of brain trauma;and predicting future neurodegeneration and treatment response from genetic, clinical, and imaging biomarkers. Building on powerful accepted technologies, our deliverable, the NA-MIC Kit, consists of software, documentation, methodology, license policy, training materials, and data. This free open-source software (FOSS) platform features novel image analysis algorithms, smooth interoperability between its components, ease of integration with third party software, and multi-pronged outreach mechanisms to facilitate end use, thus supporting basic science.

Public Health Relevance

The overarching topic of this competitive renewal is the use of medical image computing for enabling personalized medicine. Computer science and biomedical experts collaborate as part of this project to put immediately usable tools with free open-source software (FOSS) license so that others may extend the goal of using medical imaging for personalized medicine.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-K (52))
Program Officer
Pai, Vinay Manjunath
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brigham and Women's Hospital
United States
Zip Code
Torgerson, Carinna M; Irimia, Andrei; Goh, S Y Matthew et al. (2015) The DTI connectivity of the human claustrum. Hum Brain Mapp 36:827-38
Irimia, Andrei; Van Horn, John Darrell (2015) Epileptogenic focus localization in treatment-resistant post-traumatic epilepsy. J Clin Neurosci 22:627-31
Whitford, Thomas J; Kubicki, Marek; Pelavin, Paula E et al. (2015) Cingulum bundle integrity associated with delusions of control in schizophrenia: Preliminary evidence from diffusion-tensor tractography. Schizophr Res 161:36-41
Irimia, Andrei; Torgerson, Carinna M; Goh, S-Y Matthew et al. (2015) Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood. Brain Imaging Behav 9:678-89
Tauscher, Sebastian; Tokuda, Junichi; Schreiber, G√ľnter et al. (2015) OpenIGTLink interface for state control and visualisation of a robot for image-guided therapy systems. Int J Comput Assist Radiol Surg 10:285-92
Tilak, Gaurie; Tuncali, Kemal; Song, Sang-Eun et al. (2015) 3T MR-guided in-bore transperineal prostate biopsy: A comparison of robotic and manual needle-guidance templates. J Magn Reson Imaging 42:63-71
McGann, Christopher; Akoum, Nazem; Patel, Amit et al. (2014) Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI. Circ Arrhythm Electrophysiol 7:23-30
Kim, Eun Young; Magnotta, Vincent A; Liu, Dawei et al. (2014) Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data. Magn Reson Imaging 32:832-44
Liangjia Zhu; Yi Gao; Appia, Vikram et al. (2014) A complete system for automatic extraction of left ventricular myocardium from CT images using shape segmentation and contour evolution. IEEE Trans Image Process 23:1340-51
Liu, Sidong; Cai, Weidong; Wen, Lingfeng et al. (2014) Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization. Comput Med Imaging Graph 38:436-44

Showing the most recent 10 out of 417 publications