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Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and
Yogesh Kumar1, Rex A Cardan1, Ho-Hsin Chang1
1Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
Diagnostics (Basel, Switzerland)
|April 12, 2025
Summary
Establishing an Automated Medical Image Processing and Analysis (AMIPA) core facility provides a scalable solution for AI-driven medical imaging research. This shared resource enhances data analysis efficiency and research productivity.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Bioinformatics
Background:
- Medical research institutions increasingly use AI for medical imaging analysis.
- Scaling AI-driven medical image analysis requires specialized expertise and infrastructure.
- A centralized core facility for Automated Medical Image Processing and Analysis (AMIPA) offers a solution.
Purpose of the Study:
- To provide a practical roadmap for establishing an AI-based AMIPA core facility.
- To share experiences in building and operating such a resource.
- To guide institutions in accelerating AI adoption in medical imaging research.
Main Methods:
- Outlining key components for replicating a successful AMIPA core facility.
- Detailing requirements for high-performance computing resources.
- Describing robust AI software pipelines and data management strategies.
Main Results:
- Successful replication requires high-performance computing, AI software pipelines, data management, and support personnel.
- Emphasis on workflow automation and reproducibility for processing large imaging datasets.
- The roadmap facilitates efficient and consistent processing of medical imaging data.
Conclusions:
- Institutions can accelerate AI adoption in imaging workflows by implementing the proposed roadmap.
- A shared AMIPA core facility enhances the quality and productivity of medical imaging research.
- This approach fosters collaboration and resource optimization within research institutions.
