DLMUSE: Robust Brain Segmentation in Seconds Using Deep Learning

Purpose To introduce an open-source deep learning brain segmentation model for fully automated brain MRI segmentation, enabling rapid segmentation and facilitating large-scale neuroimaging research. Materials and Methods In this retrospective study, a deep learning model was developed using a diverse training dataset of 1900 MRI scans (patients aged 24-93 years, with a mean of 65 years ± 11.5 [SD]; 1007 female, 893 male) with reference labels generated using a multi-atlas segmentation method with human supervision. The final model was validated using 71 391 scans from 14 studies. Segmentation quality was assessed using Dice similarity and Pearson correlation coefficients with reference segmentations. Downstream predictive performance for brain age and Alzheimer disease was evaluated by fitting machine learning models. Statistical significance was assessed using Mann-Whitney U and McNemar tests. Results The DLMUSE model achieved high correlation (r = 0.93-0.95) and agreement (median Dice scores, 0.84-0.89) with reference segmentations across the testing dataset. Prediction of brain age using DLMUSE features achieved a mean absolute error of 5.08 years, similar to that of the reference method (5.15 years, P = .56). Classification of Alzheimer disease using DLMUSE features achieved an accuracy of 89% and F1 score of 0.80, which were comparable to values achieved by the reference method (89% and 0.79, respectively). DLMUSE segmentation speed was over 10 000 times faster than that of the reference method (3.5 seconds vs 14 hours). Conclusion DLMUSE enabled rapid brain MRI segmentation, with performance comparable to that of state-of-the-art methods across diverse datasets. The resulting open-source tools and user-friendly web interface can facilitate large-scale neuroimaging research and wide utilization of advanced segmentation methods. Keywords: Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Segmentation, Application Domain, Supervised Learning, MRI, Brain/Brain Stem Supplemental material is available for this article. © RSNA, 2025.