  Whole brain segmentation from structural magnetic resonance imaging (MRI) is
a prerequisite for most morphological analyses, but is computationally intense
and can therefore delay the availability of image markers after scan
acquisition. We introduce QuickNAT, a fully convolutional, densely connected
neural network that segments a \revision{MRI brain scan} in 20 seconds. To
enable training of the complex network with millions of learnable parameters
using limited annotated data, we propose to first pre-train on auxiliary labels
created from existing segmentation software. Subsequently, the pre-trained
model is fine-tuned on manual labels to rectify errors in auxiliary labels.
With this learning strategy, we are able to use large neuroimaging repositories
without manual annotations for training. In an extensive set of evaluations on
eight datasets that cover a wide age range, pathology, and different scanners,
we demonstrate that QuickNAT achieves superior segmentation accuracy and
reliability in comparison to state-of-the-art methods, while being orders of
magnitude faster. The speed up facilitates processing of large data
repositories and supports translation of imaging biomarkers by making them
available within seconds for fast clinical decision making.
