FastPlex: A fast deep-learning tool to map the choroid plexus in brain scans
Official title: FastPlex: A Fast Deep Learning Segmentation Method for Accurate Choroid Plexus Morphometry
Funded at University of Rochester · NIH-11473274 · Data updated 2026-11-12
A new open-source deep-learning method will quickly and accurately segment the choroid plexus in brain MRIs to study aging and Alzheimer’s-related brain changes.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Rochester NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | June 1, 2023 |
| Project end | March 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $558,766 |
| NIH project number | 7R01MH131586-04 |
| Project ID | NIH-11473274 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are adults and older adults whose brain imaging data may be part of research or clinical studies on aging or Alzheimer’s disease.
✕Less likely to help
Individuals without access to advanced MRI data or who are not involved in studies of aging or Alzheimer’s disease may not directly benefit.
What this research studies
This project develops a fast, generalizable AI tool to precisely outline the choroid plexus in high-resolution brain images. By using large neuroimaging datasets from the Human Connectome Project and other sources, it aims to improve how researchers measure this brain structure across ages and in Alzheimer’s disease. The tool will be open-source so clinicians and researchers can apply it to study brain aging and disease-related changes more reliably. The work focuses on reducing labeling errors and enabling robust analyses across diverse brain scans.
Why it matters
Potential benefit: If successful, clinicians and researchers will have a reliable, rapid way to study ChP changes in aging and Alzheimer’s, potentially informing diagnosis and understanding of disease mechanisms.
How similar studies have gone: Previous work shows imaging-based segmentation tools can improve measurement accuracy, but ChP-specific segmentation remains challenging, making this approach promising but novel.
Where this research is happening
-
University of Rochester
Rochester, United States Active
Researchers
- Principal investigator: Lizano, Paulo L — University of Rochester
- Study coordinator: Lizano, Paulo L
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.