Matt McCormick, Ph.D.

Principal Engineer

Matt McCormick, Ph.D., is a principal engineer on Kitware’s Medical Computing Team located in Carrboro, North Carolina. His experience spans multiple medical, biological, material science, and geospatial imaging applications. As a subject matter expert, he manages and makes technical contributions to scientific image analysis projects. He has been a principal investigator and a co-investigator of several research grants from the National Institutes of Health (NIH), led engagements with United States national laboratories, and he has led various commercial projects providing advanced software for medical devices. 

Matt specializes in diagnostic ultrasound imaging, with an emphasis on radio-frequency-based signal characterization. Many of his projects focus on designing and developing innovative, artificial intelligence (AI) solutions for tissue characterization, elastography, and low-cost, portable systems.

In addition to his projects, Matt also leads the development of the Insight Toolkit (ITK), a high performance, N-dimensional image processing library written in C++ with interfaces in Python and JavaScript. In development for over two decades, ITK is a collaborative effort of the world’s best research software engineers. The open source project provides hundreds of advanced algorithm modules, covering topics in image processing, registration, segmentation, quantification, and reconstruction. Matt has led the community through two major revisions, ITK 4.0 and ITK 5.0, and he has coordinated over 30 releases of the toolkit, each comprising developments from 30 to 60 individual contributors.

Recently, Matt architected a port of the toolkit to WebAssembly, called itk.js, to couple ITK with interactive browser visualizations built on vtk.js. These are built into a next generation open-source software system for medical and scientific image, mesh, and point set visualization, the itk-vtk-viewer.

Matt is also involved in Kitware’s Open Source Software Sustainability RoadShow which visits organizations like the Chan-Zuckerberg Initiative and NVIDIA to present our lessons learned on software sustainability

Matt also serves as a reviewer for journals, such as the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Medical Imaging and the Journal of Open Source Software. He is also an active contributor to conferences for the Medical Imaging Computing and Computer Assisted Intervention (MICCAI) and Scientific Computing in Python (SciPy) communities. Additionally, Matt mentors Kitware interns and serves on Ph.D. thesis committees.

During his studies at the University of Wisconsin-Madison, Matt’s research covered vascular mechanics, signal processing, medical imaging physics, and computing. His doctoral thesis focused on the characterization of carotid plaque (a primary cause of stroke) with diagnostic ultrasound. The principal aim of his research was to develop algorithms to quantify local deformation in the plaque from raw ultrasound image data. The code developed for this purpose makes extensive use of ITK and the Visualization Toolkit (VTK).

While earning his bachelor’s degree at Marquette, Matt interned at Boston Scientific Corporation, where he worked on peripheral vascular nitinol stents. 

Matt received his Ph.D. and master’s degree in biomedical engineering from the University of Wisconsin-Madison in 2011 and 2007, respectively. In 2005, he received his bachelor’s degree in biomedical engineering from Marquette University.

Education

Ph.D. in biomedical engineering from the University of Wisconsin-Madison, 2011

M.S. in biomedical engineering from University of Wisconsin-Madison, 2007

B.S. in biomedical engineering from Marquette University, 2005

Awards

Distinguished Reviewer Award, IEEE Transactions on Medical Imaging, 2021

Outstanding Teacher Award, University of Wisconsin-Madison’s Department of Medical Physics, 2009

Invited Talks & External Recognition

“Modern Insights from Microscopy Images: An Introduction to Web-based Methodologies,” Invited talk, From Images to Knowledge with ImageJ & Friends, 2020

“Scientific Image Analysis and Visualization with Dask and ITK,” Workshop, The Dask Developer Workshop, 2020

“itkwidgets: Interactive, 3D Visualization for The Age of Big Data,” Invited talk, Renaissance Computing Institute at the University of North Carolina, 2019

“itkwidgets, pyimagej, and clEsperanto: Interactive, 3D Visualization for The Age of Big Data,” Invited talk, Center for Systems Biology Dresden, 2019
“Interactive Analysis and Visualization of Large Image in the Web Browser,” Invited talk, ImageXD, Berkeley Institute for Data Science (BIDS), 2018

Get to Know Matt

What is your favorite thing about working at Kitware? My favorite part about working at Kitware is the friendly, fun, and remarkably talented people.

What do you love most about what you do? I love to engage with the world-wide community of research software engineers who create open source software that advances scientific understanding.

Share something interesting about yourself that is not on your resume. My marathon personal record is 3 hours and 45 minutes. I ran with other Kitwareans Bill Hoffman, Jake Stookey, and Rusty Blue in Vermont.

Professional Associations & Service

  • Vision, visualization, and imaging symposium chair, SciPy: Scientific Computing With Python International Conference, 2014
  • Birds of a Feather committee chair, SciPy, 2014
  • Program committee co-chair, SciPy, 2012-2013
  • Organizing member, UW-Madison The Hacker Within, 2010-2011
  • President, Alpha Eta Mu Beta (National Biomedical Engineering Honor Society), 2004-2005
  • Local Chapter Secretary, Alpha Eta Mu Beta, 2003-2004

 

Publications

  1. D. Zukić, M. Jackson, D. Dimiduk, S. Donegan, M. Groeber, and M. McCormick, "ITKMontage: A Software Module for Image Stitching," Integrating Materials and Manufacturing Innovation, Mar. 2021. [URL]
  2. D. Ushizima, M. McCormick, and D. Parkinson, "Accelerating Microstructural Analytics with Dask for Volumetric X-ray Images," in 2020 IEEE/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC), 2020. [URL]
  3. Y. Chen-Wiegart, S. Campbell, K. Yager, Y. Liu, A. Frenkel, L. Yang, J. Bai, M. McCormick, D. Allan, and M. Basham, "Multi-Modal Synchrotron Characterization: Modern Techniques and Data Analysis," in Handbook on Big Data and Machine Learning in the Physical Sciences. WORLD SCIENTIFIC, 2020, pp. 39-64. [URL]
  4. R. Grothausmann, D. Zukić, M. McCormick, C. Mühlfeld, and L. Knudsen, "Enabling Manual Intervention for Otherwise Automated Registration of Large Image Series," in Biomedical Image Registration. Springer International Publishing, 2020, pp. 23-33. [URL]
  5. T. Buchacker, C. Mühlfeld, C. Wrede, W. Wagner, R. Beare, M. McCormick, and R. Grothausmann, "Assessment of the Alveolar Capillary Network in the Postnatal Mouse Lung in 3D Using Serial Block-Face Scanning Electron Microscopy," Frontiers in Physiology, vol. 10, pp. 1357, Nov. 2019. [URL]
  6. P. Yushkevich, A. Pashchinskiy, I. Oguz, S. Mohan, J. Schmitt, J. Stein, D. Zukić, J. Vicory, M. McCormick, N. Yushkevich, N. Schwartz, Y. Gao, and G. Gerig, "User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP," Neuroinformatics, vol. 17, no. 1, pp. 83-102, Jan. 2019. [URL]
  7. P. Hernandez-Cerdan, B. Paniagua, J. Prothero, J. Marron, E. Livingston, T. Bateman, and M. McCormick, "Methods for quantitative characterization of bone injury from computed-tomography images," in submitted to Society of Photographic Instrumentation Engineers (SPIE) in Medical Imaging Conference 2019, 2019.
  8. D. CodyMorris, D. Chan, M. Palmeri, T. Glass, M. McCormick, K. JackTay, T. Polascik, R. Gupta, and K. Nightingale, "Correlation Between 3D ARFI and Quantitative Imaging Metrics from SWEI and Multi-Parametric MRI in Vivo in Normal and Cancerous Prostate Tissue," in 2018 IEEE International Ultrasonics Symposium (IUS), 2018. [URL]
  9. J. Prothero, M. McCormick, B. Paniagua, J. Vimort, A. Ruellas, J. Marron, L. Cevidanes, and E. Benavides, "Detection of bone loss via subchondral bone analysis," in Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2018. [URL]
  10. J. Fillion-Robin, M. McCormick, O. Padron, M. Smolens, M. Grauer, and M. Sarahan, "jcfr/scipy_2018_scikit-build_talk: SciPy 2018 Talk | scikit-build: A Build System Generator for CPython C/C++/Fortran/Cython Extensions," presented at SciPy 2018, 2018. [URL]
  11. H. Greer, S. Gerber, M. Niethammer, R. Kwitt, M. McCormick, D. Chittajallu, N. Siekierski, M. Oetgen, K. Cleary, and S. Aylward, "Scoliosis screening and monitoring using self contained ultrasound and neural networks," in Proceedings of the IEEE International Symposium on Biomedical Imaging, 2018. [URL]
  12. D. Chittajallu, M. McCormick, S. Gerber, T. Czernuszewicz, R. Gessner, M. Willis, M. Niethammer, R. Kwitt, and S. Aylward, "Image-based methods for phase estimation, gating and temporal super-resolution of cardiac ultrasound," IEEE Transactions on Biomedical Engineering, pp. 1-1, 2018. [URL]
  13. A. Patwardhan, R. Brandt, S. Butcher, L. Collinson, D. Gault, K. Grünewald, C. Hecksel, J. Huiskonen, A. Iudin, M. Jones, P. Korir, A. Koster, I. Lagerstedt, C. Lawson, D. Mastronarde, M. McCormick, H. Parkinson, P. Rosenthal, S. Saalfeld, H. Saibil, S. Sarntivijai, I. Solanes Valero, S. Subramaniam, J. Swedlow, I. Tudose, M. Winn, and G. Kleywegt, "Building bridges between cellular and molecular structural biology," eLife, vol. 6, pp. e25835, Jul. 2017. [URL]
  14. M. M McCormick, M. L Palmeri, J. Fillion-Robin, and S. Aylward, "SlicerITKUltrasound: A 3D Slicer extension for scan conversion of B-mode and next-generation ultrasound imaging modalities," The Journal of Open Source Software, vol. 2, no. 10, pp. 153, Feb. 2017. [URL]
  15. S. Gerber, M. Jallais, H. Greer, M. McCormick, S. Montgomery, B. Freeman, D. Kane, D. Chittajallu, N. Siekierski, and S. Aylward, "Automatic Estimation of the Optic Nerve Sheath Diameter from Ultrasound Images," in Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound. Springer International Publishing, 2017, pp. 113-120. [URL]
  16. M. Jallais, H. Greer, S. Gerber, M. McCormick, D. Chittajallu, N. Siekierski, and S. Aylward, "Ultrasound Augmentation: Rapid 3-D Scanning for Tracking and On-Body Display," in Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound. Springer International Publishing, 2017, pp. 138-145. [URL]
  17. J. Vimort, M. McCormick, and B. Paniagua, "Computing Bone Morphometric Feature Maps from 3-Dimensional Images," Insight Journal, no. January-December 2017, 2017. [URL]
  18. J. Vimort, M. McCormick, F. Budin, and B. Paniagua, "Computing Textural Feature Maps for N-Dimensional images," Insight Journal, 2017. [URL]
  19. M. Palmeri, T. Glass, R. Gupta, M. McCormick, A. Brown, T. Polascik, S. Rosenzweig, A. Buck, and K. Nightingale, "Comparison between 3D ARFI imaging and mpMRI in detecting clinically-significant prostate cancer lesions," in 2016 IEEE International Ultrasonics Symposium (IUS), 2016. [URL]
  20. S. Aylward, M. McCormick, H. Kang, S. Razzaque, R. Kwitt, and M. Niethammer, "Ultrasound spectroscopy," in Proceedings of the IEEE International Symposium on Biomedical Imaging, 2016. [URL]
  21. D. Zukić, M. McCormick, G. Gerig, and P. Yushkevich, "RLEImage: run-length encoded memory compression scheme for an itk::Image," The Insight Journal, 2016. [URL]
  22. D. Zukić, J. Vicory, M. McCormick, L. Wisse, G. Gerig, P. Yushkevich, and S. Aylward, "nD morphological contour interpolation," The Insight Journal, 2016. [URL]
  23. X. Liu, M. Niethammer, R. Kwitt, N. Singh, M. McCormick, and S. Aylward, "Low-Rank Atlas Image Analyses in the Presence of Pathologies," IEEE Transactions on Medical Imaging, vol. 34, no. 12, pp. 2583-2591, Dec. 2015. [URL]
  24. M. McCormick, X. Liu, J. Jomier, C. Marion, and L. Ibanez, "ITK: enabling reproducible research and open science," Frontiers in Neuroinformatics, vol. 8, 2014. [URL]
  25. X. Liu, M. Niethammer, R. Kwitt, M. McCormick, and S. Aylward, "Low-Rank to the Rescue – Atlas-Based Analyses in the Presence of Pathologies," in Proceedings of the Internation Conference on Medical Image Computing and Computer Assisted Intervention, 2014. [URL]
  26. L. Ibanez, X. Liu, and M. McCormick, "ITK Bar Camp: Growing the ITK Community," Kitware Source, no. 24, Jan. 2013.
  27. M. McCormick, "New infrastructure for easy multi-threading in itkv4," Kitware Source, no. 24, Jan. 2013.
  28. M. McCormick and T. Varghese, "An Approach to Unbiased Subsample Interpolation for Motion Tracking," Ultrasonic Imaging, vol. 35, no. 2, pp. 76-89, Apr. 2013. [URL]
  29. B. Rocque, D. Jackson, T. Varghese, B. Hermann, M. McCormick, M. Kliewer, C. Mitchell, and R. Dempsey, "Impaired cognitive function in patients with atherosclerotic carotid stenosis and correlation with ultrasound strain measurements," Journal of the Neurological Sciences, vol. 322, no. 1-2, pp. 20-24, Nov. 2012. [URL]
  30. M. McCormick, T. Varghese, X. Wang, C. Mitchell, M. Kliewer, and R. Dempsey, "Methods for robust in vivo strain estimation in the carotid artery," Physics in Medicine and Biology, vol. 57, no. 22, pp. 7329-7353, Nov. 2012. [URL]
  31. B. Avants, N. Tustison, G. Song, B. Wu, M. Stauffer, M. McCormick, H. Johnson, and J. Gee, "A Unified Image Registration Framework for ITK," in Biomedical Image Registration. Springer Berlin Heidelberg, 2012, pp. 266-275. [URL]
  32. X. Liu, B. Helba, K. Krishnan, P. Reynolds, M. McCormick, W. Turner, L. Ibanez, D. Yankelevitz, and R. Avila, "Fostering open science in lung cancer lesion sizing with ITK module LSTK," The Insight Journal, 2012. [URL]
  33. M. McCormick, N. Rubert, and T. Varghese, "Bayesian Regularization Applied to Ultrasound Strain Imaging," IEEE Transactions on Biomedical Engineering, vol. 58, no. 6, pp. 1612-1620, Jun. 2011. [URL]
  34. M. McCormick, "A lightweight image comparison library," Kitware Source, no. 16, Jan. 2011.
  35. M. McCormick, E. Madsen, M. Deaner, and T. Varghese, "Absolute backscatter coefficient estimates of tissue-mimicking phantoms in the 5–50 MHz frequency range," The Journal of the Acoustical Society of America, vol. 130, no. 2, pp. 737-743, Aug. 2011. [URL]
  36. M. McCormick, E. Madsen, M. Deaner, and T. Varghese, "Absolute backscatter coefficient estimates of tissue-mimicking phantoms in the 5–50 MHz frequency range," The Journal of the Acoustical Society of America, vol. 130, no. 2, pp. 737-743, Aug. 2011. [URL]
  37. E. Madsen, G. Frank, M. McCormick, M. Deaner, and T. Stiles, "Anechoic sphere phantoms for estimating 3-D resolution of very-high-frequency ultrasound scanners," IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 57, no. 10, pp. 2284-2292, Oct. 2010. [URL]
  38. M. McCormick, "Visual debugging of ITK," Kitware Source, no. 13, Apr. 2010.
  39. M. McCormick, "An Open Source, Fast Ultrasound B-Mode Implementation for Commodity Hardware," Insight Jounral, no. January-June 2010, 2010. [URL]
  40. M. McCormick, "Higher Order Accurate Derivative and Gradient Calculation in ITK," Insight Journal, no. July-December 2010, 2010. [URL]
  41. H. Shi, T. Varghese, C. Mitchell, M. McCormick, R. Dempsey, and M. Kliewer, "In vivo attenuation and equivalent scatterer size parameters for atherosclerotic carotid plaque: Preliminary results," Ultrasonics, vol. 49, no. 8, pp. 779-785, Dec. 2009. [URL]
  42. H. Shi, C. Mitchell, M. McCormick, M. Kliewer, R. Dempsey, and T. Varghese, "Preliminary in vivo atherosclerotic carotid plaque characterization using the accumulated axial strain and relative lateral shift strain indices," Physics in Medicine and Biology, vol. 53, no. 22, pp. 6377-6394, Nov. 2008. [URL]

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