  Whole brain segmentation on structural magnetic resonance imaging (MRI) is
essential for understanding neuroanatomical-functional relationships.
Traditionally, multi-atlas segmentation has been regarded as the standard
method for whole brain segmentation. In past few years, deep convolutional
neural network (DCNN) segmentation methods have demonstrated their advantages
in both accuracy and computational efficiency. Recently, we proposed the
spatially localized atlas network tiles (SLANT) method, which is able to
segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN
segmentation methods yield inferior performance under external validations,
especially when the testing patterns were not presented in the training
cohorts. Recently, we obtained a clinically acquired, multi-sequence MRI brain
cohort with 1480 clinically acquired, de-identified brain MRI scans on 395
patients using seven different MRI protocols. Moreover, each subject has at
least two scans from different MRI protocols. Herein, we assess the SLANT
method's intra- and inter-protocol reproducibility. SLANT achieved less than
0.05 coefficient of variation (CV) for intra-protocol experiments and less than
0.15 CV for inter-protocol experiments. The results show that the SLANT method
achieved high intra- and inter- protocol reproducibility.
