Manual, semi-automated, and automated delineation of chronic brain lesions: a comparison of methods

The exact delineation of chronic brain lesions is a crucial step when investigating the relationship between brain structure and (dys-)function. For this, manual tracing, although very time-consuming, is still the gold standard. In order to assess the possible contributions from other methods, we compared manual tracing of lesion boundaries with a newly developed semi-automated and a fully automated approach for lesion definition in a sample of chronic stroke patients (n=11, 5m, median age 12, range 10-30years). Manual tracing requires substantially more human input (4.8-9.6h/subject) than semi-automated (24.9min/subject) and automated processing (1min/subject). When compared with manual tracing as the gold standard, both the semi-automated (tested with 4 different smoothing filters) and the automated approach towards lesion definition performed on an acceptable level, with an average Dice's similarity index of .53-.60 (semi-automated) and .49 (automated processing). In all semi-automated and automated approaches, larger lesions were identified with a significantly higher performance than smaller lesions, as were central versus peripheral voxels, indicating that the surface-to-volume ratio explains this trend. The automated approach failed to identify two lesions. In several cases, indirect lesion effects (such as enlarged ventricles) were detected using the semi-automated or the automated approach. We conclude that manual tracing remains the gold standard for exact lesion delineation, but that semi-automated and automated approaches may be alternatives for larger lesions and/or larger studies. The detection of indirect lesion effects may be another application of such approaches in the future.