FY2010 - Oak Ridge National Laboratory

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Director’s R&D Fund— Ultrascale Computing and Data Science techniques; and (3) a searchable index of the AAA reports that includes section-specific fields (i.e., 'Findings,' 'Impression'), dates, and measurements. The image-processing portion of the project developed techniques for high-performance (1) mammogram analysis, and (2) AAA analysis, providing the ability to extract image features describing biological tissue abnormalities. For mammography, the following techniques were implemented for multicore processing: fractal encoding, fractal encoding segmentation, histogram analysis, region growing/snakes, waveletbased multiresolution segmentation, watershed, and pattern analysis. The image features support the framework’s need for a large set of potentially relevant observables that are combined with the text analysis in the machine-learning environment. The AAA effort focused on the development of automated and semi-automated segmentation tools allowing extraction of (1) the 3D position of the vessels wall (aorta, femoral arteries, renal arteries, and peripheral vasculature); (2) the blood flow position within the vessels wall; and (3) the endovascular stent graft. The image analysis was performed on low- to highresolution CT scan stacks. Implemented techniques include bilateral filtering, connected component grouping, fast marching, and level-set segmentation. Preliminary work has been done on the quantitative measurement of the vascular structures. In year 2, we developed a framework for conducting architecture-level dependability analysis of DAMSEL. A system-level fault tree was developed, qualitative analysis was conducted, and a failure modes and effects analysis (FMEA) was conducted. As a result of these analyses, we identified potential failure modes for the system (see Pullum et al). The project also resulted in a collaboration with the University of Tennessee-Knoxville on a Joint Directed Research and Development (JDRD) project led by Itamar Arel, PhD (PI) on the development of an alternative semi-supervised learning system using Deep Belief Networks. This collaboration between the LDRD and the JDRD allowed us to optimize our algorithms for the learning framework and test the data sets in different environments. Changes in Research Plan (optional) Biomedical applications were changed in year 2 to include AAAs and analysis of endoleaks. Work was performed in year 1 on Traumatic Brain Injury, using the Joint Theater Trauma Registry, but lack of access to correlating images prevented the multimodality data analysis and integration. An opportunity arose to work with a data set on AAAs, including CT images and radiology reports and operative notes. This replaced the proposed work on TBI. This data set represented longitudinal patient records ranging from 2–5 years after initial AAA repair. Institutional Review Board (IRB) approval was received for all members of the team to have access to the datasets. New team members were also added, including Carlos Rojas and Laura Pullum, Computational Sciences and Engineering Division, and a summer student, Jillian Gauld (Queens University, Kingston, ON). This new application provided a rich source for developing, testing, and validating the methodology and helped provide significant results in year 2. A validation effort to assess the dependability of the DAMSEL architecture was added in year 2. Grants Several proposals for funding, based on the results of this project and multimodality data integration concept, are in progress. One involves collaboration with the University of North Carolina, and the other is collaboration with the University of Tennessee Graduate School of Medicine and Emory University (Center for Comprehensive Informatics) on a grant to be submitted to the National Institutes of Health (NIH) in February 2011. In addition, follow-on efforts intended to expand the applicability of the multimodal learning framework to a broad range of medical decision support applications have been proposed as part of a recently awarded NIH center grant in which ORNL has a part. 68
Director’s R&D Fund— Ultrascale Computing and Data Science Information Shared Beckerman, Barbara G., Shaun S. Gleason, Ryan A. Kerekes, April D. McMillan, Robert M. Nishikawa, Vincent C. Paquit, Robert M. Patton, and Christopher T. Symons. 2009. Data Analytics for Medicine Using Semi-Supervised Learning, LDRD Report, LOIS # 05237, Oak Ridge National Laboratory, Oak Ridge, TN, November. Beckerman, Barbara. 2010. “Multi-Modality Data Analytics for Medicine.” Guest seminar at Department of Medical Physics, Duke University, January. Beckerman, Barbara, and Brian Worley. 2010. “ORNL Overview and Biomedical/DAMSEL Overview.” Invited presentation at University of Tennessee Health Sciences Center, April. Gauld, Jillian S., and Barbara G. Beckerman. 2010. “Analyzing Endoleaks and Predicting Outcomes after Aneurysm Repair.” Poster presentation by RAMS students, August. Gauld, Jillian S., and Barbara G. Beckerman. 2010. “Applying Data Mining Methods to Blast-Related Mild Traumatic Brain Injury.” Proc. of the Biomedical Sciences and Engineering Center Conference 2010, Oak Ridge, TN, May 25–26 (IEEE Xplore). Patton, R. M., B. G. Beckerman, and T. E. Potok. 2009. “Analysis of Mammography Reports Using Maximum Variation Sampling.” Proc. of the 4th GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), ACM Press, New York. Patton, R. M., B. G. Beckerman, and T. E. Potok. 2009. “Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm.” First Annual ORNL Biomedical Science & Engineering Conference, Mar. 18–19, Oak Ridge, TN, IEEE Catalog Number CFP0947G. Patton, R. M., T. E. Potok, B. G. Beckerman, and J. N. Treadwell. 2009. “A Genetic Algorithm for Learning Significant Phrase Patterns in Radiology Reports.” Proceedings of the 5th GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation (MedGEC), ACM Press, New York. Patton, Robert M., and Thomas E. Potok. 2010. “Discovering Potential Precursors of Mammography Abnormalities Based on Textual Features, Frequencies, and Sequences.” 10th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2010). Patton, Robert M., Barbara G. Beckerman, Thomas E. Potok, and Jim N. Treadwell. 2010. “Genetic Algorithm for Analysis of Abdominal Aortic Aneurysms in Radiology Reports.” Proc. of the 6th GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation in Conjunction with the 2010 Genetic and Evolutionary Computation Conference (GECCO 2010). Pullum, Laura L., Christopher T. Symons, Robert M. Patton, and Barbara G. Beckerman. 2010. “Architecture-Level Dependability Analysis of a Medical Decision Support System.” Int. Conf. on Software Engineering (ICSE) Workshop on Software Engineering in Health Care (SEHC '10), Cape Town, South Africa, May. Symons, C., R. Kerekes, V. C. Paquit, R. Patton, S. Gleason, and B. Beckerman. 2009. “A Multi-modal, Semi-supervised Learning System for Building Better Decision Support Systems for the Analysis of Mammograms.” 95 th Annual Meeting of the Radiological Society of North America (RSNA), Chicago, Nov. 29–Dec. 5. Patton, R. M, T. E. Potok, and B. G. Beckerman. 2010. “Detecting Temporal Precursor Words in Text Documents Using Wavelet Analysis.” U.S. Patent Application 61/310,351, filed March 4. Patton, R. M., and T. E. Potok. 2010. “Method for Learning Phrase Patterns from Textual Documents.” U.S. Patent Application 61/331,941, filed May 6. 69
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FY2010 - Oak Ridge National Laboratory

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