Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles (SLANT) approach has been shown to effectively segment whole brain non-contrast T1w MRI with 132 volumetric labels. Enhancing generalizability of SLANT would enable broader application of volumetric assessment in multi-site studies. Transfer learning (TL) is commonly to update neural network weights for local factors; yet, it is commonly recognized to risk degradation of performance on the original validation/test cohorts. Here, we explore TL by data augmentation to address these concerns in the context of adapting SLANT to anatomical variation (e.g., adults versus children) and scanning protocol (e.g., non-contrast research T1w MRI versus contrast-enhanced clinical T1w MRI). We consider two datasets: First, 30 T1w MRI of young children with manually corrected volumetric labels, and accuracy of automated segmentation defined relative to the manually provided truth. Second, 36 paired datasets of pre- and post-contrast clinically acquired T1w MRI, and accuracy of the post-contrast segmentations assessed relative to the pre-contrast automated assessment. For both studies, we augment the original TL step of SLANT with either only the new data or with both original and new data. Over baseline SLANT, both approaches yielded significantly improved performance (pediatric: 0.89 vs. 0.82 DSC, p<0.001; contrast: 0.80 vs 0.76, p<0.001 ). The performance on the original test set decreased with the new-data only transfer learning approach, so data augmentation was superior to strict transfer learning.
Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances
in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a
trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at
millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the
hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right
hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each
lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for
incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation
per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus.
This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75).
Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body
volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R<sup>2</sup> greater than 0.95, p
< 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement
of the hippocampus and other temporal lobe structures.