Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs

Model performance results. Plots show aggregate metrics across image types for each testing cohort (see Supplemental Data for results for image type separately); error bars represent SEM. A, Standard metrics for segmentation evaluation including Dice similarity, sensitivity, and 95% Hausdorff distance. B, Average performance ratings based on visual inspection by 2 raters (1 = fully covered, 0.75 = approximately 75% masked, 0.5 = 50% masked, 0.25 = 25% masked, 0 = not masked at all).

Representative example images of model predicted versus manual ground truth segmentation masks. Subjects shown with high (left box; T1W-CE sequence) and low (right box; FLAIR sequence) Dice similarity scores between the model predicted (upper row) and manual ground truth (lower row) face masks. This illustrates how Dice score, although a common metric for such segmentation tasks, was not an accurate measure of model performance in the present study, as ground truth masks were variable in their extension into space in front of the face (particularly due to “MiDeface” lettering imposed by the MiDeface Freesurfer tool that was used to generate initial face masks).