Direct segmentation of brain white matter tracts in diffusion MRI

TL;DR

This paper introduces a deep learning approach for direct segmentation of brain white matter tracts from diffusion MRI data, avoiding complex intermediate steps and improving accuracy and generalizability in clinical settings.

Contribution

The authors propose a novel deep learning method that segments white matter tracts directly from diffusion MRI, bypassing traditional intermediate computations and enhancing robustness across different data acquisition protocols.

Findings

Achieves state-of-the-art segmentation accuracy (mean Dice 0.826).

Demonstrates superior generalizability to undersampled and varied protocol data.

Introduces a new method for detecting inaccurate segmentations.

Abstract

The brain white matter consists of a set of tracts that connect distinct regions of the brain. Segmentation of these tracts is often needed for clinical and research studies. Diffusion-weighted MRI offers unique contrast to delineate these tracts. However, existing segmentation methods rely on intermediate computations such as tractography or estimation of fiber orientation density. These intermediate computations, in turn, entail complex computations that can result in unnecessary errors. Moreover, these intermediate computations often require dense multi-shell measurements that are unavailable in many clinical and research applications. As a result, current methods suffer from low accuracy and poor generalizability. Here, we propose a new deep learning method that segments these tracts directly from the diffusion MRI data, thereby sidestepping the intermediate computation errors. Our experiments show that this method can achieve segmentation accuracy that is on par with the state of the art methods (mean Dice Similarity Coefficient of 0.826). Compared with the state of the art, our method offers far superior generalizability to undersampled data that are typical of clinical studies and to data obtained with different acquisition protocols. Moreover, we propose a new method for detecting inaccurate segmentations and show that it is more accurate than standard methods that are based on estimation uncertainty quantification. The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.