Regional Deep Atrophy: Using Temporal Information to Automatically Identify Regions Associated with Alzheimer’s Disease Progression from Longitudinal MRI
Mengjin Dong, Long Xie, Laura Wisse, Robin de Flores, Sandhitsu R Das, David A Wolk, Paul A Yushkevich · 2025
Read Regional Deep Atrophy: Using Temporal Information to Automatically Identify Regions Associated with Alzheimer’s Disease Progression from Longitudinal MRI by Mengjin Dong, Long Xie, Laura Wisse, Robin de Flores, Sandhitsu R Das, David A Wolk, Paul A Yushkevich (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11712647.txt.
Description
Mengjin Dong and co-authors present a deep learning method that measures brain shrinkage linked to Alzheimer's disease in a 2025 paper. Tracking shrinkage of the hippocampus over time is a known marker of the disease. Standard tools compare scans directly and can mistake head motion or image flaws for real tissue loss.
Their method, Regional Deep Atrophy, learns from pairs of scans taken at different times. It learns to tell which scan came later and whether the gap in time matches the change seen. The network also marks the regions that drive its judgment, then measures volume change only there. Trained and tested on hundreds of participants, it matched prior tools. The design aims for cleaner measures in early disease.
Catalog record
| CREATOR | Mengjin Dong, Long Xie, Laura Wisse, Robin de Flores, Sandhitsu R Das, David A Wolk, Paul A Yushkevich |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 3 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |