LAUSR

Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U‐Net) and a large‐scale database consisting of 7039 brain T1‐weighted MRI data samples. We evaluated the method by using experiments focused on three distinct topics, namely, the necessity of preprocessing steps, cross‐institutional and longitudinal reproducibility, and volumetric accuracy. The optimized model, MX_RW—where “MX” is a mix of RW and nonuniform intensity normalization data and “RW” is raw data with basic preprocessing—did not require time‐consuming preprocessing steps, such as nonuniform intensity normalization or image registration, for brain MRI before segmentation. Cross‐institutional testing revealed that MX_RW (Dice similarity coefficient: 0.809, coefficient of variation: 4.6%, and Pearson's correlation coefficient: 0.979) exhibited a performance comparable with that of FreeSurfer (Dice similarity coefficient: 0.798, coefficient of variation: 5.6%, and Pearson's correlation coefficient: 0.973). The computation time per dataset of MX_RW was generally less than 5 s (even when tested without graphics processing units), which was notably faster than FreeSurfer. Thus, for time‐restricted applications, MX_RW represents a competitive alternative to FreeSurfer.