Lawrence H. Staib, PhD

Associate Professor of Diagnostic Radiology, Biomedical Engineering, and Electrical Engineering

Contact

Address:
Yale University, School of Medicine
Department of Diagnostic Radiology
P.O.Box 208042, 333 Cedar Street
New Haven, Connecticut 06520-8042
United States

Email: lawrence.staib@yale.edu
Telephone: (203) 785-2427
Fax: (203) 737-4273

Education:

PhD Electrical Engineering, Yale University, 1990.

Please click here for Curriculum Vitae

Research Interests

My research centers on techniques for accurate analysis and quantification of medical images. Current medical imaging modalities can reveal rich information about structure and function in three dimensions and in vivo. However, in order to extract measures that are meaningful for scientific or clinical purposes, it is necessary to have quantitative methods of medical image analysis that are robust in the presence of noise, complexity, artifacts, etc. I am developing methods for the analysis of structure in images using geometric models of deformable objects to reveal geometric and functional features that may be indicative of disease. Image segmentation, registration, and shape analysis are key problem areas where appropriate match metrics and models must be formulated. I am also interested in the analysis of diffusion tensor magnetic resonance images and functional magnetic resonance images. I am applying these techniques primarily to the measurement of neuroanatomy and function. More accurate, reproducible, and efficient methods for measurement from images will aid in the basic understanding of physiology and in the identification of structural and functional correlates of disease.

My work concerns the development and application of algorithms for the analysis of biomedical images for quantification of structure and function. Structural image analysis methods of interest include statistical and geometric deformable models for segmentation and model-based shape measurement and comparison. Diffusion weighted magnetic resonance image analysis also provides structural information; important problems here include white matter parcellation, quantification, and fiber tracking. Groups of subjects can be characterized both structurally and functionally by statistical characterization and classification using spatial patterns of structural and functional parameters.

Selected Publications

1. Boundary finding with parametrically deformable models. Staib, L.H.; Duncan, J.S.; Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 14, Issue 11, Nov. 1992 Page(s):1061 - 1075.
2. Regional Brain Volume Abnormalities and Long-term Cognitive Outcome in Preterm Infants. Bradley S. Peterson, MD; Betty Vohr, MD; Lawrence H. Staib, PhD; Christopher J. Cannistraci, BA; Aaron Dolberg, BA; Karen C. Schneider, MPH; Karol H. Katz, MS; Michael Westerveld, PhD; Sara Sparrow, PhD; Adam W. Anderson, PhD; Charles C. Duncan, MD; Robert W. Makuch, PhD; John C. Gore, PhD; Laura R. Ment, MD. JAMA 2000;284:1939-1947.
3. Neural correlates of exposure to traumatic pictures and sound in Vietnam combat veterans with and without posttraumatic stress disorder: a positron emission tomography study. J. Douglas Bremner ,Lawrence H. Staib, Danny Kaloupek, Steven M. Southwick, Robert Soufer and Dennis S. Charney. Biological Psychiatry Volume 45, Issue 7, 1 April 1999, Pages 806-816.
4. .MR Imaging of the Temporal Stem: Anatomic Dissection Tractography of the Uncinate Fasciculus, Inferior Occipitofrontal Fasciculus, and Meyer’s Loop of the Optic Radiation. E. Leon Kier, Lawrence H. Staib, Lawrence M. Davis and Richard A. Bronen. American Journal of Neuroradiology 25:677-691, May 2004.
5. Boundary finding with prior shape and smoothness models. Yongmei Wang; Staib, L.H.; Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume 22, Issue 7, July 2000 Page(s):738 - 743.
6. White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging. Marcel Jackowski, Chiu Yen Kao, Maolin Qiu, R. Todd Constable and Lawrence H. Staib. Medical Image Analysis, Volume 9, Issue 5, October 2005, Pages 427-440.
7. Geometric strategies for neuroanatomic analysis from MRI. James S. Duncan, Xenophon Papademetris, Jing Yang, Marcel Jackowski, Xiaolan Zeng and Lawrence H. Staib. NeuroImage Volume 23, 2004, Pages S34-S45.
8. Nonlinear Estimation and Modeling of fMRI Data Using Spatio-temporal Support Vector Regression. Yongmei Michelle Wang, Robert T. Schultz, R. Todd Constable and Lawrence H. Staib. Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Volume 2732 (Year 2003), Information Processing in Medical Imaging Pages 647-659.
9. Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors. Bansal, R. Staib, L.H. Dongrong Xu Laine, A.F. Royal, J. Peterson, B.S. Medical Imaging, IEEE Transactions on Publication Date: May 2008 Volume: 27, Issue: 5 page(s): 589-607.
10. Integrated Intensity and Point-Feature Nonrigid Registration. Xenophon Papademetris , Andrea P. Jackowski, Robert T. Schultz, Lawrence H. Staiband James S. Duncan. Lecture Notes in Computer Science Springer Berlin / Heidelberg. Volume 3216 Medical Image Computing and Computer-Assisted Intervention. MICCAI 2004 Pages 763-770 2004.

For a further list of Staib's publications, please see PubMed and Google Scholar.

Current and Former Trainees

Gary Ho
Yongmei Wang
Eliezer Kahn
Xianzhang Lei
Marcel Jackowski
Lei Zhang
Krystal Holderness
Claire Mathieu
Feng Ma
Debayan Datta