  Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a
fundamental way researchers study the brain in a host of neurological
conditions including normal maturation and aging. Despite the availability of
open-source brain segmentation software, widespread clinical adoption of
volumetric analysis has been hindered due to processing times and reliance on
manual corrections. Here, we extend the use of deep learning models from
proof-of-concept, as previously reported, to present a comprehensive
segmentation of cortical and deep gray matter brain structures matching the
standard regions of aseg+aparc included in the commonly used open-source tool,
Freesurfer. The work presented here provides a real-life, rapid deep
learning-based brain segmentation tool to enable clinical translation as well
as research application of quantitative brain segmentation. The advantages of
the presented tool include short (~1 minute) processing time and improved
segmentation quality. This is the first study to perform quick and accurate
segmentation of 102 brain regions based on the surface-based protocol (DMK
protocol), widely used by experts in the field. This is also the first work to
include an expert reader study to assess the quality of the segmentation
obtained using a deep-learning-based model. We show the superior performance of
our deep-learning-based models over the traditional segmentation tool,
Freesurfer. We refer to the proposed deep learning-based tool as DARTS
(DenseUnet-based Automatic Rapid Tool for brain Segmentation). Our tool and
trained models are available at https://github.com/NYUMedML/DARTS
