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Diagnostics
Volume 15
Issue 7
10.3390/diagnostics15070803
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Technical Note

Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications

by
Yogesh Kumar
1,*,
Rex A. Cardan
1,
Ho-hsin Chang
1,
Katherine A. Heinzman
1,2,3,
Kadir Gultekin
2,
Amy Goss
4,
Andrew McDonald
1,3,
Donna Murdaugh
3,5,
Jonathan McConathy
6,
Steven Rothenberg
6,
Andrew D. Smith
6,7,
John Fiveash
1 and
Carlos E. Cardenas
1,*
1
Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
2
Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
3
Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL 35233, USA
4
Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35233, USA
5
Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
6
Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
7
Department of Radiology, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
*
Authors to whom correspondence should be addressed.
Diagnostics 2025, 15(7), 803; https://doi.org/10.3390/diagnostics15070803
Submission received: 3 February 2025 / Revised: 12 March 2025 / Accepted: 19 March 2025 / Published: 21 March 2025
(This article belongs to the Special Issue Medical Images Segmentation and Diagnosis)
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Abstract

Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility—a shared institutional resource—dedicated to Automated Medical Image Processing and Analysis (AMIPA). Methods: This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. Results: We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. Conclusions: By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research.
Keywords: advanced medical imaging workflows; imaging core facility advanced medical imaging workflows; imaging core facility

Share and Cite

MDPI and ACS Style

Kumar, Y.; Cardan, R.A.; Chang, H.-h.; Heinzman, K.A.; Gultekin, K.; Goss, A.; McDonald, A.; Murdaugh, D.; McConathy, J.; Rothenberg, S.; et al. Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications. Diagnostics 2025, 15, 803. https://doi.org/10.3390/diagnostics15070803

AMA Style

Kumar Y, Cardan RA, Chang H-h, Heinzman KA, Gultekin K, Goss A, McDonald A, Murdaugh D, McConathy J, Rothenberg S, et al. Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications. Diagnostics. 2025; 15(7):803. https://doi.org/10.3390/diagnostics15070803

Chicago/Turabian Style

Kumar, Yogesh, Rex A. Cardan, Ho-hsin Chang, Katherine A. Heinzman, Kadir Gultekin, Amy Goss, Andrew McDonald, Donna Murdaugh, Jonathan McConathy, Steven Rothenberg, and et al. 2025. "Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications" Diagnostics 15, no. 7: 803. https://doi.org/10.3390/diagnostics15070803

APA Style

Kumar, Y., Cardan, R. A., Chang, H.-h., Heinzman, K. A., Gultekin, K., Goss, A., McDonald, A., Murdaugh, D., McConathy, J., Rothenberg, S., Smith, A. D., Fiveash, J., & Cardenas, C. E. (2025). Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications. Diagnostics, 15(7), 803. https://doi.org/10.3390/diagnostics15070803

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