James C. Gee

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Associate Professor of Radiologic Science in Radiology
Director, Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
Co-Director, Translational Biomedical Imaging Center, Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
Director, HHMI-NIBIB Interfaces Program in Biomedical Imaging and Informational Sciences, University of Pennsylvania, Philadelphia, PA
Department: Radiology

Contact information
Department of Radiology
Penn Image Computing and Science Laboratory
Richards - 6th Floor
3700 Hamilton Walk
Philadelphia, PA 19104-6116
Office: 215-746-6295
Education:
B.S. (Electrical Engineering)
University of Washington, 1987.
B.S. (Computer Science)
University of Washington, 1987.
M.S. (Electrical Engineering)
University of Washington, 1989.
Ph.D. (Computer and Information Science)
University of Pennsylvania, 1996.
Post-Graduate Training
Visiting Scholar, Laboratoire des Signaux et Images en Medecine, Faculte de Medecine, Universite de Rennes I, Rennes, France, 1994-1995.
Postdoctoral Fellow in Neurology and Computer and Information Science, University of Pennsylvania, 1996-1997.
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Description of Itmat Expertise

Dr. Gee's major area of interest is biomedical image analysis and computing, with active research in all of the quantitative methods represented, including segmentation, registration, morphometry and shape statistics, as applied to a variety of organ systems and all of the major and emerging modalities in biological/biomaterials imaging and in vivo medical imaging.

Selected Publications

Phillips, J. S., Da Re, F., Irwin, D. J., McMillian, C. T., Vaishnavi, S. N., Xie, S. X., Lee, E. B., Cook P.A., Gee, J. C., Shaw, L. M., Trojanowski, J. Q., Wolk, D. A., Grossman, M. : Longitudinal progression of grey matter atrophy in non-amnestic Alzheimer's disease. BRAIN: A Journal of Neurology 142((6)): 1701-1722, Sep 2019.

Johnson, G. A., Wang, N., Anderson, R.J., Chen, M., Cofer, G.P., Gee, J. C., Pratson, F., Tustison, N., White, L. E.: Whole mouse brain connectomics. Journal of Comparative Neurology 527(13): 2146-2157, Sep 2019.

Duong M T, Rudie J D, Wang J, Xie L, Mohan S, Gee J C, Rauschecker A M: Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging. AJNR. American journal of neuroradiology 40(8): 1282-1290, Aug 2019.

Duong, M T; Rudie, J D; Wang, J; Xie, L; Mohan, S; Gee, J C; Rauschecker, A M : Convolutional neural network for automated FLAIR lesion segmentation on clinical brain MR imaging. American Journal of Neurology 40(8): 1282 - 1290, Aug 2019.

Tustison, N. J., Avants, B. B., Gee, J. C.: Learning image-based spatial transformations via convolutional neural networks: A review. Magnetic Resonance Imaging 64(142-153), Dec 2019 Notes: doi: 10.1016/j.mri.2019.05.037. Epub 2019 Jun 11.

Claeson AA, Vresilovic EJ, Showalter BL, Wright AC, Gee JC, Malhotra NR, Elliott DM: Human Disc Nucleotomy Alters Annulus Fibrosus Mechanics at Both Reference and Compressed Loads. J Biomech Eng 141(11), May 2019.

Guo, Y., Chung, F., Li, G., Wang, J., Gee, J.C., : Leveraging label-specific discriminant mapping features for multi-label learning. ACM Transactions on Knowledge Discovery from Data 13(2): art. no. 24 pp1-23, Apr 2019.

Tustison, N. J., Avants, B. B., Lin, Z., Feng, X., Cullen, N., Mata, J. F., Flos, L., Gee, J. C., Altes, T. A., Mugler, III, J. P., Qing, K.: Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification. Academic Radiology 26(3): 412-423, Mar 2019 Notes: doi: 10.1016/j.acra.2018.08.003. Epub 2018 Sep 6.

Kim H, Goo JM, Ohno Y, Kauczor HU, Hoffman EA, Gee JC, van Beek EJR: Effect of Reconstruction Parameters on the Quantitative Analysis of Chest Computed Tomography J Thorac Imaging 34(2): 92-102, March 2019.

Lindsay, W.D., Ahern, C.A., Tobias, J.S., Berlind, C.G., Chinniah, C., Gabriel, P.E., Gee, J.C., Simone, C.B.: Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy. Medical Physics 46(2): 1054-1063, Feb 2019.

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Last updated: 09/30/2020
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