Advances in digital imaging technologies have led to a substantial growth in the number of digital images being created and stored in hospitals, medical systems, and on the Internet in recent years. Effective medical image retrieval systems can play an important role in teaching, research, diagnosis and treatment. Images were historically retrieved using text-based methods. The quality of annotations associated with images can reduce the effectiveness of text-based image retrieval. Despite recent advances, purely content- based image retrieval techniques lag significantly behind their textual counterparts in their ability to capture the semantic essence of the user's query. Preliminary research suggests that a more promising approach is to adaptively combine these complementary techniques to suit the user and their information needs. However, for these approaches to succeed, the researcher needs to enhance her computational skills in addition to acquiring a comprehensive understanding of the relevant clinical domain. This Pathway to Independence (K99/R00) grant application describes a training and career development plan that will allow the candidate, an NLM postdoctoral fellow in Medical Informatics at Oregon Health &Science University to achieve these objectives. The training component will be carried out under the mentorship of Dr. W. Hersh with Dr. Gorman (user studies). Dr. Fuss (radiation medicine) and Dr. Erdogmus (machine learning) providing additional mentoring in their areas of expertise. The long-term goal of this Pathway to Independence (K99/R00) project is to improve visual information retrieval by better understanding user needs and proposing adaptive methodologies for multimodal image retrieval that will close the semantic gap. During the award period, activities will be focused on the following specific aims: (1) Understand the image retrieval needs of novice and expert users in radiation oncology and develop gold standards for evaluation;(2) Develop algorithms for semantic, multimodal image retrieval;(3) Perform user based evaluation of adaptive image retrieval in radiation oncology;(4) Extend the techniques developed to create a multimodal image retrieval system in pathology

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Transition Award (R00)
Project #
5R00LM009889-05
Application #
8522304
Study Section
Special Emphasis Panel (ZLM1-ZH-C (M3))
Program Officer
Sim, Hua-Chuan
Project Start
2009-09-15
Project End
2014-09-14
Budget Start
2013-09-15
Budget End
2014-09-14
Support Year
5
Fiscal Year
2013
Total Cost
$216,041
Indirect Cost
$91,879
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Obuchowski, Nancy A; Reeves, Anthony P; Huang, Erich P et al. (2015) Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons. Stat Methods Med Res 24:68-106
Obuchowski, Nancy A; Barnhart, Huiman X; Buckler, Andrew J et al. (2015) Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example. Stat Methods Med Res 24:107-40
Bolón-Canedo, V; Ataer-Cansizoglu, E; Erdogmus, D et al. (2015) Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach. Comput Methods Programs Biomed 122:1-15
Kalpathy-Cramer, Jayashree; de Herrera, Alba García Seco; Demner-Fushman, Dina et al. (2015) Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013. Comput Med Imaging Graph 39:55-61
Awan, Musaddiq; Dyer, Brandon Alan; Kalpathy-Cramer, Jayashree et al. (2015) Auto-segmentation of the brachial plexus assessed with TaCTICS - a software platform for rapid multiple-metric quantitative evaluation of contours. Acta Oncol 54:557-60
Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S et al. (2015) Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles. Methods Inf Med 54:93-102
Mohamed, Abdallah S R; Ruangskul, Manee-Naad; Awan, Musaddiq J et al. (2015) Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms. Radiology 274:752-63
Kalpathy-Cramer, Jayashree; Freymann, John Blake; Kirby, Justin Stephen et al. (2014) Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. Transl Oncol 7:147-52
Kalpathy-Cramer, Jayashree; Gerstner, Elizabeth R; Emblem, Kyrre E et al. (2014) Advanced magnetic resonance imaging of the physical processes in human glioblastoma. Cancer Res 74:4622-4637
Pinho, Marco C; Polaskova, Pavlina; Kalpathy-Cramer, Jayashree et al. (2014) Low incidence of pseudoprogression by imaging in newly diagnosed glioblastoma patients treated with cediranib in combination with chemoradiation. Oncologist 19:75-81

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