Senior Associate Consultant, Neuroradiology
Assistant Professor of Medicine, Mayo Clinic College of Medicine and Science
I have a passion for computer technology and a background in software engineering that I brought to medical school. Throughout my medical career I have sought to combine my medical and technological skills, mainly because years later I still love making computers do new things.
Radiology has been a very good fit in this regard. Nearly every project I undertake involves something novel technologically. In fellowship, I became involved in 3D printing and medical image segmentation to create more interactive educational experiences. I have carried this forward into my practice at UNMC, creating educational lectures presented nationally, as well as hands-on labs locally. Early on as staff, I also began creating online quizzes with scrollable radiology studies, during which I learned to process DICOM studies in Python.
With my base interests in medical image segmentation and processing, I was naturally very excited by the rise of convolutional neural networks (CNNs) as a means for automatically performing the otherwise tedious task of segmentation.
Current Research Interest:
As alluded to above, my research in artificial intelligence (AI) is focused on medical image processing. My primary goal is the creation of technologies that have the potential to directly improve the workflow and performance of radiologists, which directly translates to improved patient care.
Given my background, I have largely focused on medical image segmentation. Our first project was to segment the ventricles on CT head studies. From this, I have branched in several different directions, utilizing various segmented structures to perform additional image processing tasks. My goal is to develop commercially viable software as well open source projects to benefit research and education.
Huff T, Ludwig P, Salazar D, Cramer J. Fully Automated Intracranial Ventricle Segmentation on CT with 2D Regional Convolutional Neural Network to Estimate Ventricular Volume. International Journal of Computer Assisted Radiology and Surgery. July 2019.
Cramer J, Quigley E, Hutchins T, Shah L. Educational Material for 3D Visualization of Spine Procedures: Methods for Creation and Dissemination. Journal of Digital Imaging. June 2017.
Cramer, J. A., et al. (2016). "Limitations of T2*-Gradient Recalled-Echo and Susceptibility-Weighted Imaging in Characterizing Chronic Subdural Hemorrhage in Infant Survivors of Abusive Head Trauma." AJNR Am J Neuroradiol 37(9): 1752-1756.
Shah LM, Cramer J, Ferguson MA, Anderson JS. Reproducibility of Individual Differences in Functional Connectivity Acquired During Task and Resting State. Brain and Behavior. March 2016.
Cramer J, Eisenmenger LB, Pierson NS, Dhatt HS, Heilbrun ME. Structured and Templated Reporting: An Overview. Applied Radiology. Applied Radiology. August 2014;18-21.