Professor of Radiology, the Committee on Medical Physics, and the College
Vice-Chair for Basic Science Research, Dept. of Radiology
5841 South Maryland Avenue, MC2026
Chicago, Illinois 60637
Maryellen L. Giger is presently Professor of Radiology, the Committee on Medical Physics, and the College at the University of Chicago. She also serves as Vice-Chair for Basic Science Research in the Department of Radiology, University of Chicago and Director of the BSD’s Imaging Research Institute. Involvement at the national and international level can be found in her CV.
Dr. Giger is considered one of the pioneers in the development of CAD (computer-aided diagnosis). She has authored or co-authored more than 240 scientific manuscripts (including 160 peer-reviewed journal articles), is inventor/co-inventor on approximately 25 patents, and serves as a reviewer for various national and international granting agencies, including the NIH and the U.S. Army. Dr. Giger is a member of the National Academy of Engineering (NAE) of the National Academies. She is currently chair of the SPIE Medical Imaging Symposium. Dr. Giger has been associate editor for Medical Physics and IEEE Transactions on Medical Imaging. She is an elected fellow of the American Institute for Medical and Biological Engineering (AIMBE) and the American Association of Physicists in Medicine (AAPM), and is a Senior Member of IEEE. In the recent past, she has served as the nationally-elected President and Treasurer of the AAPM and as a third Vice-President of the RSNA. For the RSNA, she has served as chair of the RSNA Research Grant Study Section and chair of the physics subcommittee for the RSNA program committee. She has given several invited presentations on CAD at SPIE, BIROW, SCAR, IWDM, CARS, AAPM, and RSNA, as well as at various international meetings, and at workshops and conferences of the NCI, as well as serving on various scientific program committees. At the University, she is an active participant within the Cancer Center and the Breast SPORE.
Giger lab focuses on the development of multimodality CAD (computer-aided diagnosis) and quantitative image analysis methods. Her research interests include digital medical imaging, computer-aided diagnosis, quantitative image analysis, and data-mining in breast imaging, chest/CT imaging, cardiac imaging, and bone radiography. The long-term goals of her research are to investigate, develop, and translate multi-modality computerized image analysis techniques, which yield image-based tumor signatures and phenotypes, for improved cancer diagnosis, prognosis, and patient care. Development of these methods includes novel means for lesion segmentation, and 2D and 3D extraction of features characterizing the tumors and local background surround. These methods include development of computerized self-assessing lesion segmentation methods, which include methods for the computer to self-assess whether or not the lesion is well segmented as well as development of methods for incorporating extracted lesions features from multiple views and/or modalities, including those that weight features by the accuracy of the corresponding segmentation and those that use Bayesian neural network (BANN) with automatic relevance determination (ARD) priors for joint feature selection and classification. The Giger lab’s research also includes an investigation of the role of quantitative breast parenchymal characteristics in computerized analysis for both diagnosis and cancer risk assessment in an attempt to understand the relationship between image-based biomarkers and biological and clinical biomarkers. Additional research involves methods for the optimization of the computer/human interface for presentation of computer output in computer-aided diagnosis (CAD). Computer-determined estimates of the probability of malignancy of lesions are dependent on the prevalence of cancer in the training database, which most often does not correspond to the prevalence of cancer in the population from which the user has experience, e.g., the population seen in the user's medical practice. Thus, the user often has difficulty interpreting the computer-estimated probability of malignancy. Thus, the Giger lab is developing approaches with which to transform computer output to those that would match the internal parameters of the reader and thus provide useful indices of the probability of malignancy. The Giger lab is also developing methods for assessing risk of fracture and osteoporosis using measures of bone mass, bone structure, and clinical data. Specifically, they aim to develop computerized radiographic methods for quantifying bone structure (through radiographic texture analysis: gRTAh) that may be used together with measures of bone mass and clinical data for use in quantitatively assessing bone strength.