  About 5-8% of individuals over the age of 60 have dementia. With our
ever-aging population this number is likely to increase, making dementia one of
the most important threats to public health in the 21st century. Given the
phenotypic overlap of individual dementias the diagnosis of dementia is a major
clinical challenge, even with current gold standard diagnostic approaches.
However, it has been shown that certain dementias show specific structural
characteristics in the brain. Progressive supranuclear palsy (PSP) and multiple
system atrophy (MSA) are prototypical examples of this phenomenon, as they
often present with characteristic brainstem atrophy. More detailed
characterization of brain atrophy due to individual diseases is urgently
required to select biomarkers and therapeutic targets that are meaningful to
each disease. Here we present a joint multi-atlas-segmentation and
deep-learning-based segmentation method for fast and robust parcellation of the
brainstem into its four sub-structures, i.e., the midbrain, pons, medulla, and
superior cerebellar peduncles (SCP), that in turn can provide detailed
volumetric information on the brainstem sub-structures affected in PSP and MSA.
The method may also benefit other neurodegenerative diseases, such as
Parkinson's disease; a condition which is often considered in the differential
diagnosis of PSP and MSA. Comparison with state-of-the-art labeling techniques
evaluated on ground truth manual segmentations demonstrate that our method is
significantly faster than prior methods as well as showing improvement in
labeling the brainstem indicating that this strategy may be a viable option to
provide a better characterization of the brainstem atrophy seen in PSP and MSA.
