  The neuroimage analysis community has neglected the automated segmentation of
the olfactory bulb (OB) despite its crucial role in olfactory function. The
lack of an automatic processing method for the OB can be explained by its
challenging properties. Nonetheless, recent advances in MRI acquisition
techniques and resolution have allowed raters to generate more reliable manual
annotations. Furthermore, the high accuracy of deep learning methods for
solving semantic segmentation problems provides us with an option to reliably
assess even small structures. In this work, we introduce a novel, fast, and
fully automated deep learning pipeline to accurately segment OB tissue on
sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we
designed a three-stage pipeline: (1) Localization of a region containing both
OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized
region through four independent AttFastSurferCNN - a novel deep learning
architecture with a self-attention mechanism to improve modeling of contextual
information, and (3) Ensemble of the predicted label maps. The OB pipeline
exhibits high performance in terms of boundary delineation, OB localization,
and volume estimation across a wide range of ages in 203 participants of the
Rhineland Study. Moreover, it also generalizes to scans of an independent
dataset never encountered during training, the Human Connectome Project (HCP),
with different acquisition parameters and demographics, evaluated in 30 cases
at the native 0.7mm HCP resolution, and the default 0.8mm pipeline resolution.
We extensively validated our pipeline not only with respect to segmentation
accuracy but also to known OB volume effects, where it can sensitively
replicate age effects.
