Improving medical imaging analysis using advanced deep learning techniques
Official title: Fast and Robust Deep Learning for Medical imaging: Segmentation and Registration methods invariant to contrast and resolution
Funded at Massachusetts General Hospital · NIH-10922812 · Data updated 2026-11-12
This study is working on new technology to help doctors get better and faster insights from medical images, which could lead to more accurate diagnoses and tailored treatment plans for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | September 6, 2023 |
| Project end | August 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $561,881 |
| NIH project number | 5R01EB033773-02 |
| Project ID | NIH-10922812 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients undergoing medical imaging procedures, particularly those requiring diverse imaging modalities.
✕Less likely to help
Patients who do not require medical imaging or those with conditions that do not involve imaging analysis may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy and efficiency of medical imaging analysis through advanced deep learning methods for segmentation and registration. It aims to develop tools that can analyze various types of medical images without being limited by contrast or resolution differences. By addressing the computational demands and biases of existing tools, this project seeks to create a more versatile imaging analysis framework that can be applied across different medical conditions and imaging modalities. Patients may benefit from improved diagnostic accuracy and personalized treatment plans as a result of this innovative approach.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and efficient medical imaging analyses, improving patient diagnosis and treatment outcomes.
How similar studies have gone: Previous research has shown promising results using deep learning techniques for medical imaging, indicating a strong potential for success in this area.
Where this research is happening
-
Massachusetts General Hospital
Boston, United States Active
Researchers
- Principal investigator: Dalca, Adrian — Massachusetts General Hospital
- Study coordinator: Dalca, Adrian
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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.