Whole Brain Protocol for Tractography with Empirical MRI

Overview

The TractEM project is creating a tractography-based whole-brain protocol that is informed by the Eve labeling protocol. We are releasing sample labeled data from the Human Connectome Project and soliciting feedback on both label quality and protocol definitions. The design criteria are:

• The region definitions should conform to the Eve definitions wherever possible.
• Regions should be driven by tractography to the greatest extent possible.
• All pathways for a single brain should be able to be manually traced in less than 6 hours.

We are preparing a manuscript based on assessing the manual reproducibility of the protocol. We welcome both feedback and authorship level participation. To join our team:

• To comment on a particular tract protocol, please use the “comment” function at the bottom of a tract’s individual page. These are manually reviewed on a weekly basis.
  • Comments/discussions will be made public unless the comment specifically asks to be private.
  • Suggestions on protocol changes are encouraged.
  • Suggestions on references (either journal articles, online resources, or offline texts) are welcome and will be integrated into the overall protocol.
• To provide your contact information or provide more detailed feedback/edits, please e-mail us using the contact form on the About Us page.
  • Comments on the scope, protocol definitions, scope of available programmatic resources, etc. are most welcome.
  • We appreciate any comments to increase usability of our platform.
• To keep up to date with protocol changes or data releases, subscribe to low volume our mailing list.
  • We will only use your contact information to send e-mail announcements related to TractEM protocol updates and releases (maximum e-mail frequency is once per week).

Scope

Multi-atlas labeling has come in wide spread use for whole brain labeling on magnetic resonance imaging. Recent challenges have shown that leading techniques are near (or at) human expert reproducibility for cortical gray matter labels. However, multi-atlas approaches tend to treat white matter as essentially homogeneous (as white matter exhibits isointense signal on structural MRI). The state-of-the-art for white matter atlas is the single-subject Johns Hopkins Eve atlas^(1,2). Numerous approaches have attempted to use tractography and/or orientation information to identify homologous white matter structures across subjects. Despite success with large tracts, these approaches have been plagued by difficulties in with subtle differences in course, low signal to noise, and complex structural relationships for smaller tracts. In preliminary work⁽³⁾, we investigated use of atlas-based labeling to propagate the Eve atlas to unlabeled datasets. We evaluated both single atlas labeling and multi-atlas labeling using synthetic atlases derived from the single manually labeled atlas.

Labeling white matter regions of interest poses interesting challenges relative to other anatomical structures^{(1, 2)} due to the need to integrate both local and global information. The Eve atlas provides labels along with fractional anisotropy (FA) maps, T1 weighted structural MRI, and multi-modal information. There are two primary barriers to use of Eve within atlas-based frameworks: (1) only one subject is labeled, and (2) the peripheral white matter regions are conservatively labeled (Figure 1).

Figure 1. Example renderings of (A) BrainCOLOR cortical and sub-cortical labels, (B) Eve cortical and white matter labels and (C) rectified BrainCOLOR and Eve cortical and white matter labels.

Our work⁽³⁾ presented an initial study of atlas-based labeled with Eve. On 5 representative tracts for 10 subjects, we demonstrate that (1) single atlas labeling generally provides segmentations within 2mm mean surface distance, (2) morphologically constraining DTI labels within structural MRI white matter reduces variability, and (3) multi-atlas labeling did not improve accuracy. These efforts present a preliminary indication that single atlas labels with correction is reasonable, but caution should be applied. To purse multi-atlas labeling and more fully characterize overall performance, more labeled datasets would be necessary.

Findings should be treated as preliminary given the limited number of tracts that were manually labels and the difficulty of generalizing to all Eve white matter regions. In general, results were reassuring with mean surface distances of <2mm. However, Hausdorff distances were much greater (>10mm). Rectification with an automated multi-atlas approach focusing gray matter reduced outliers. Perhaps surprisingly, manually selected “good” single atlas results did not provide an effective alternative to multi-atlas labeling. We concluded that continued validation of Eve atlas propagation hinges upon the availability of additional subjects labeled with an equivalent protocol. As an additional benefit, true multi-atlas approaches would be possible with independently labeled subjects.

This project proposes to rectify the lack of manually available data. First, a written protocol will be developed to apply the Mori et al protocol (written in 2008) using software tools compatible with currently available image analysis platforms. Second, 10 (ten) subjects from the Human Connectome Project with DTI and structural MRI will be manually labeled to enable characterization the protocol on data with exceptionally high data quality and resolution. Finally, an electronic report will be prepared describing how to access and use each of the deliverables.

⁽¹⁾ Mori S, Oishi K, Jiang H, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage. 2008;40(2):570–82.

⁽²⁾ Oishi K, Faria A, Jiang H, et al. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. Neuroimage. 2009;46(2):486–99.

⁽³⁾Plassard AJ, Hinton KE, Venkatraman V, Gonzalez C, Resnick SM, Landman BA. “Evaluation of Atlas-Based White Matter Segmentation with Eve.” Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. pii: 94133E.