The goal of the Training Core (Pis Randy Gollub, MGH and Sona Pujol, BWH) is to lower barriers to effective communication between the clinical translational investigators and the software engineers engaged in the development and application of medical image analysis and data management software tools for NA-MIC. These communities have diverse educational backgrounds and often do not share a common vocabulary or forum for exchanging ideas or valuable tools and solutions. The Training Core addresses this gap by educating members of the biomedical clinical and research communities in the domains of knowledge relevant to the application of medical image analysis and its interface with computer science. We began this task six years ago by focusing on the most effective means for our own internal NA-MIC team to communicate. First we identified critical domains of knowledge that were necessary for all to comprehend. Then we developed an effective means for rapidly bringing everyone to a high level of competence in these domains. For instance, during our first Project Event (Programmer's Week 2004), we educated the image algorithm developers interested in diffusion tractography about functional and structural neuroanatomy, focusing explicltiy on the major fiber tracts. During all large group meetings we made it a policy to pause to define unfamiliar or potentially ambiguous terms until we had arrived at a common vocabulary. The continuing evolution from that initial gathering to our most recent Project Events is clear testament to the success ofthis approach. Although packaged as a training exercise for the NA-MIC Kit, the primary activify of the Training Core in the first NA-MIC funding cycle was to develop and deliver hands-on experiential learning experiences to clinicians, algorithm developers, and computer scientists to increase their competence in all aspects of medical image analysis. Thus, we used components ofthe NA-MIC Kit, primarily the 3D Slicer software, to teach the fundamentals of applied medical image processing and visualization. All of our tutorials can be self-taught or administered by an instmctor. Each tutorial follows the rubric established in How People Learn (1, 2,3), which requires learner-centered, goal-oriented experiential teaching. This approach enabled us to develop a single set of training materials that were equally well suited for constituents from all backgrounds, i.e., clinicians, statisticians, and computer scientists. These tools further served to strengthen communication among these communities by defining and promulgating common vocabulary. On the basis of positive feedback from attendees of past workshops, we believe that facilitating the adoption ofthe NA-MIC Kit for education, research, and medical image analysis software development will have a tremendous positive impact on clinical translational medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54EB005149-08
Application #
8377490
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
8
Fiscal Year
2012
Total Cost
$230,401
Indirect Cost
$101,325
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
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
Xia, K; Zhang, J; Ahn, M et al. (2017) Genome-wide association analysis identifies common variants influencing infant brain volumes. Transl Psychiatry 7:e1188
Fishbaugh, James; Durrleman, Stanley; Prastawa, Marcel et al. (2017) Geodesic shape regression with multiple geometries and sparse parameters. Med Image Anal 39:1-17

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