Learning white matter subject-specific segmentation from structural MRI

Abstract

Purpose: Mapping brain white matter (WM) is essential for building an understanding of brain anatomy and function. Tractography-based methods derived from diffusion-weighted MRI (dMRI) are the principal tools for investigating WM. These procedures rely on time-consuming dMRI acquisitions that may not always be available, especially for legacy or time-constrained studies. To address this problem, we aim to generate WM tracts from structural magnetic resonance imaging (MRI) image by deep learning.

Methods: Following recently proposed innovations in structural anatomical segmentation, we evaluate the feasibility of training multiply spatial localized convolution neural networks to learn context from fixed spatial patches from structural MRI on standard template. We focus on six widely used dMRI tractography algorithms (TractSeg, RecoBundles, XTRACT, Tracula, automated fiber quantification (AFQ), and AFQclipped) and train 125 U-Net models to learn these techniques from 3870 T1-weighted images from the Baltimore Longitudinal Study of Aging, the Human Connectome Project S1200 release, and scans acquired at Vanderbilt University.

Results: The proposed framework identifies fiber bundles with high agreement against tractography-based pathways with a median Dice coefficient from 0.62 to 0.87 on a test cohort, achieving improved subject-specific accuracy when compared to population atlas-based methods. We demonstrate the generalizability of the proposed framework on three externally available datasets.

Conclusions: We show that patch-wise convolutional neural network can achieve robust bundle segmentation from T1w. We envision the use of this framework for visualizing the expected course of WM pathways when dMRI is not available.

Keywords: T1 weight MRI; learning methods and patch-wise deep neural network; tractography algorithms; white matter.

FIGURE 1

(a) White matter (WM) is largely homogenous when imaged using most sources of MRI contrast, for example T1 weighted (T1w) (left).(b) Traditional WM atlas (center) represents each voxel with one tissue class. (c) Modern approaches at bundle segmentation identify multiple overlapping structures (right). Diffusion tractography offers the ability to capture a multi-label description of WM voxels

FIGURE 2

The pipeline of proposed white matter (WM) bundle learning is presented, which integrates data processing and registration as well as bundle learning. We extract WM bundles from six different tractography methods. Structural images and corresponding tractograms are reoriented to the Montreal Neurological Institute (MNI) template. Patch-wise, spatial-localized neural networks are utilized to learn WM bundle regions from a T1 weighted (T1w) MRI image. The output of each U-net is merged as the final step before segmentation. Representative samples of WM bundles acquired from six automatic tractography methods, and the final learning result is visualized

FIGURE 3

Each curve represents the average DSC of all white matter (WM) bundles of all validation dataset scans per diffusion tractography algorithms for multi-atlas segmentation (MAS), atlas-, and learning-based methods at different threshold values. The 95% confidence interval is within the printed notch due to large sample population size. The legend above the each plot includes the optimal threshold for each tractography algorithms

FIGURE 4

3D visualization of multi-atlas segmentation (MAS), atlas-, and learning-based results across six diffusion tractography algorithms by reconstruction of the left corticospinal tract (CST) surface on an affine-reoriented coronal T1 weighted (T1w) MRI slice. The text below each image is quantitative DSC for each case

FIGURE 5

Quantitative results of multi-atlas segmentation (MAS), atlas-based method, and proposed learning methods on test cohorts from Human Connectome Project (HCP), Baltimore Longitudinal Study of Aging (BLSA), and Vanderbilt University (VU) and external cohort from HCP_LS, IXI, and UG. The outlier percentage (top row) of all six algorithms is shown in bar plot. Two measures are used to assess the overlap between algorithms deriving fiber mask from T1 weighted (T1w) and truth from diffusion-weighted MRI (dMRI): Dice (middle row) and surface distance (lower row). Each column presents the result of a different bundle segmentation algorithm and shows the proposed method, MAS, and single atlas-based method. Each boxplot includes each pathway of the bundle segmentation algorithm per every scan. The 95% confidence interval is within the printed notch due to large sample size. The difference between methods was significant (p < 0.005, Wilcoxon signed-rank test, indicated by *)

FIGURE 6

Plots of overlap versus overreach for the left corticospinal tract (CST) across all bundle segmentation algorithms for multi-atlas segmentation (MAS), atlas-, and learning-based methods are shown. The markers on each curve to represent the overlap and overreach values at specific threshold values. The range of overreach for MAS is (0, 6). The range of overreach for atlas-based methods is (0, 9). The range of overreach for the learning-based method is (0, 6)

FIGURE 7

Each curve represents average DSC of all white matter (WM) bundles of all external dataset scans per diffusion tractography algorithm for atlas- and learning-based methods. The 95% confidence interval is within the printed line width due to large sample size. The legend above each plot includes the optimal threshold for each tractography algorithms