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.

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 #
8501010
Study Section
Special Emphasis Panel (ZRG1-BST-K (52))
Program Officer
Pai, Vinay Manjunath
Project Start
2004-09-17
Project End
2014-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
9
Fiscal Year
2013
Total Cost
$3,687,550
Indirect Cost
$373,552
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Ghayoor, Ali; Vaidya, Jatin G; Johnson, Hans J (2018) Robust automated constellation-based landmark detection in human brain imaging. Neuroimage 170:471-481
Wachinger, Christian; Toews, Matthew; Langs, Georg et al. (2018) Keypoint Transfer for Fast Whole-Body Segmentation. IEEE Trans Med Imaging :
Lyu, Ilwoo; Perdomo, Jonathan; Yapuncich, Gabriel S et al. (2018) Group-wise Shape Correspondence of Variable and Complex Objects. Proc SPIE Int Soc Opt Eng 10574:
Hong, Sungmin; Fishbaugh, James; Gerig, Guido (2018) 4D CONTINUOUS MEDIAL REPRESENTATION BY GEODESIC SHAPE REGRESSION. Proc IEEE Int Symp Biomed Imaging 2018:1014-1017
Swanson, Meghan R; Wolff, Jason J; Shen, Mark D et al. (2018) Development of White Matter Circuitry in Infants With Fragile X Syndrome. JAMA Psychiatry 75:505-513
Swanson, Meghan R; Shen, Mark D; Wolff, Jason J et al. (2018) Naturalistic Language Recordings Reveal ""Hypervocal"" Infants at High Familial Risk for Autism. Child Dev 89:e60-e73
Swanson, Meghan R; Shen, Mark D; Wolff, Jason J et al. (2017) Subcortical Brain and Behavior Phenotypes Differentiate Infants With Autism Versus Language Delay. Biol Psychiatry Cogn Neurosci Neuroimaging 2:664-672
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
Rutherford, Helena J V; Maupin, Angela N; Landi, Nicole et al. (2017) Parental reflective functioning and the neural correlates of processing infant affective cues. Soc Neurosci 12:519-529

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