EAGLE-I aims to remove some of the ambiguity and inter-rater bias that dominates visual quality checking (QC) of cortical parcellations in neuroimaging. Here we present:
- A systematic method for the search and identification of errors.
- Clear rules for classifying and recording errors in each brain region.
- Automated brain level quality ratings using region level error counts.
EAGLE-I is created in addition to four QC resources previously developed and is not designed to be used as a stand-alone QC guide. Before implementing this protocol, we recommend users read the ENIGMA Cortical QC (ENQC) guide and the FreeSurfer tutorial. EAGLE-I combines the strengths of previous methods whilst addressing collective limitations.
- Evelyn Deutscher
- Karen Caeyenberghs
We would like to thank the following people who spent countless hours using EAGLE-I to perform QC. Their experiences and feedback have been invaluable for shaping this resource
- Jake Burnett
- Lyndon Firman-Sadler
- Annalee Cobden
- Michael Pink
- Finian Keleher
- Emma Read
- Courtney McCabe
- Janine Lyons
If you find this tool useful in your research, please reference our manuscript:
Deutscher, Evelyn et al. "ENIGMA's advanced guide for parcellation error identification (EAGLE-I): An implementation in the context of brain lesions." MethodsX vol. 15 103482. 4 Jul. 2025, doi:10.1016/j.mex.2025.103482 (https://www.sciencedirect.com/science/article/pii/S2215016125003279)