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Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is afundamental way researchers study the brain in a host of neurologicalconditions including normal maturation and aging. Despite the availability ofopen-source brain segmentation software, widespread clinical adoption ofvolumetric analysis has been hindered due to processing times and reliance onmanual corrections. Here, we extend the use of deep learning models fromproof-of-concept, as previously reported, to present a comprehensivesegmentation of cortical and deep gray matter brain structures matching thestandard regions of aseg+aparc included in the commonly used open-source tool,Freesurfer. The work presented here provides a real-life, rapid deeplearning-based brain segmentation tool to enable clinical translation as wellas research application of quantitative brain segmentation. The advantages ofthe presented tool include short (~1 minute) processing time and improvedsegmentation quality. This is the first study to perform quick and accuratesegmentation of 102 brain regions based on the surface-based protocol (DMKprotocol), widely used by experts in the field. This is also the first work toinclude an expert reader study to assess the quality of the segmentationobtained using a deep-learning-based model. We show the superior performance ofour 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 andtrained models are available at https://github.com/NYUMedML/DARTS

DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation