DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

TL;DR

DARTS is a deep learning tool that rapidly and accurately segments 102 brain regions from MRI scans, significantly reducing processing time and improving segmentation quality for clinical and research use.

Contribution

This work introduces DARTS, a novel DenseUnet-based deep learning model that enables fast, accurate segmentation of extensive brain regions, surpassing traditional methods like Freesurfer.

Findings

DARTS processes brain MRI scans in about 1 minute.

DARTS outperforms Freesurfer in segmentation accuracy.

Expert reader study confirms high quality of DARTS segmentations.

Abstract

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