ENIGMA’s advanced guide for parcellation error identification (EAGLE-I): An implementation in the context of brain lesions - Evelyn Deutscher; Emily Dennis; Jake Burnett; Lyndon Firman-Sadler; Annalee L. Cobden; Michael Pink; Finian Keleher; Emma Read; Courtney McCabe; Janine Lyons; Frank G. Hillary; Elisabeth A. Wilde; et al. (MethodsX, 2025)
Cortical parcellation is a critical step in several neuroimaging pipelines, yet even in high quality images without pathology, errors are common. For patients with moderate to severe traumatic brain injury (ms-TBI), typical parcellation errors can be exacerbated by focal pathology impacting cortical regions. Careful visual quality checking (QC) of parcellation images of ms-TBI patients should be routinely conducted to identify the presence of parcellation errors across regions (region error identification). Researchers must also determine if the amount of error identified warrants the exclusion of that image (image quality rating). However, previous QC methods have applied somewhat ambiguous rules for region error identification and inconsistent thresholds for image quality ratings.We developed ENIGMA’s Advanced Guide for parceLlation Error Identification (EAGLE-I) - a detailed training resource for identifying, classifying, and recording different error types within each individual region.Region level errors are identified by: (a) type, unconnected (affecting a single ROI) or connected (affecting multiple ROIs); (b) size (minor, intermediate, or major); and (c) directionality, overestimation or underestimation.Region level errors are recorded on a user friendly customised spreadsheet with standardised coding that subsequently enables automatic calculation of brain quality rating (pass, minor error, major error, fail, or discuss).