  Many clinical and research studies of the human brain require an accurate
structural MRI segmentation. While traditional atlas-based methods can be
applied to volumes from any acquisition site, recent deep learning algorithms
ensure very high accuracy only when tested on data from the same sites
exploited in training (i.e., internal data). The performance degradation
experienced on external data (i.e., unseen volumes from unseen sites) is due to
the inter-site variabilities in intensity distributions induced by different MR
scanner models, acquisition parameters, and unique artefacts. To mitigate this
site-dependency, often referred to as the scanner effect, we propose LOD-Brain,
a 3D convolutional neural network with progressive levels-of-detail (LOD) able
to segment brain data from any site. Coarser network levels are responsible to
learn a robust anatomical prior useful for identifying brain structures and
their locations, while finer levels refine the model to handle site-specific
intensity distributions and anatomical variations. We ensure robustness across
sites by training the model on an unprecedented rich dataset aggregating data
from open repositories: almost 27,000 T1w volumes from around 160 acquisition
sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old.
Extensive tests demonstrate that LOD-Brain produces state-of-the-art results,
with no significant difference in performance between internal and external
sites, and robust to challenging anatomical variations. Its portability opens
the way for large scale application across different healthcare institutions,
patient populations, and imaging technology manufacturers. Code, model, and
demo are available at the project website.
