IBM estimates that 30% of the entire data in the world is medical information. Medical images occupy a significant portion of medical records with approximately 100 million scans in US and growing every year. In addition, the data size from each scan steadfastly increases as the image resolution improves. These BigData are not structured and due to lack of standardized imaging protocols, they are highly heterogeneous with different spatial resolutions, contrasts, slice orientations, etc. In this project, we will deelop a technology to structure and search medical imaging information, which will make the past data available for education and evidence-based clinical decision-making. In this grant, we will focus on brain MRI, which comprises the largest portion of MRI data. The target community will be physicians who make decisions and the patients will be the ultimate beneficiaries. Currently, radiological image data are stored in clinical database called PACS. The image data in PACS are not structured. Consequently, once the diagnosis of a patient is completed, most of the data in PACS are currently discarded in the archive. Radiologists rely on their experience and education to reach medical decisions. This is a typical problem in medical practice that calls for objective evidence-based medicine. There are many ongoing attempts to structure the text fields of PACS, which include natural language processing of free-text radiological reports, clinical information, and diagnosis. In our approach, we propose to structure the image data, not text fields, to support direct search of images. Namely, physicians will submit an image of a new patient and search past images with similar anatomical phenotypes. Then, the clinical reports of the retrieved data will be compiled for a statistical report of the diagnosis and prognosis. We believe this image structuration is the key to """"""""unlock the vast amounts of information currently stored"""""""" in PACS and use them for education and modern evidence-based medical decisions.
The specific aims are;Objective 1: To develop and test the accuracy of high-throughput image structuration technologies Objective 2: To develop and test the image search engine Objective 3: Capacity Building Requirement: To develop prototype cloud system for data structuration / search services for research and educational purposes

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-N (52))
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Pai, Vinay Manjunath
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Johns Hopkins University
Engineering (All Types)
Schools of Engineering
United States
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Wu, Dan; Ceritoglu, Can; Miller, Michael I et al. (2016) Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting. Neuroimage Clin 12:570-581
Wu, Dan; Ma, Ting; Ceritoglu, Can et al. (2016) Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. Neuroimage 125:120-30
Miller, Michael I; Trouvé, Alain; Younes, Laurent (2015) Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson. Annu Rev Biomed Eng 17:447-509
Tang, Xiaoying; Crocetti, Deana; Kutten, Kwame et al. (2015) Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles. Front Neurosci 9:61
Nowrangi, Milap A; Okonkwo, Ozioma; Lyketsos, Constantine et al. (2015) Atlas-based diffusion tensor imaging correlates of executive function. J Alzheimers Dis 44:585-98
Liang, Zifei; He, Xiaohai; Ceritoglu, Can et al. (2015) Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool. PLoS One 10:e0133533
Faria, Andreia V; Oishi, Kenichi; Yoshida, Shoko et al. (2015) Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis. Neuroimage Clin 7:367-76
Tang, Xiaoying; Yoshida, Shoko; Hsu, John et al. (2014) Multi-contrast multi-atlas parcellation of diffusion tensor imaging of the human brain. PLoS One 9:e96985
Zhang, Yajing; Chang, Linda; Ceritoglu, Can et al. (2014) A Bayesian approach to the creation of a study-customized neonatal brain atlas. Neuroimage 101:256-67
Zhang, Yajing; Zhang, Jiangyang; Hsu, Johnny et al. (2014) Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): application to normal and Alzheimer's populations. Neuroimage 84:406-19

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