Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

Seven anatomic bundles of twelve example subjects.

<p>Seven anatomic bundles which were obtained through manual labeling for left hemispheres of twelve example subjects.</p

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

<div><p>We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.</p></div

Overview of the proposed approach to automatic classification.

<p>The method consists of two parts: Example data construction and automatic tract classification.</p

Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

Sensitivity histogram for tract group and direct tract labeling.

<p>The x-axis and y-axis represents sensitivity ranges and percentage of bundles that are included in the corresponding sensitivity ranges, respectively.</p

Skeleton of our automatic classification algorithm.

<p>The skeleton of the voting algorithm to determine the label of an input group.</p

Comparison with a Guevara et al.’s method [33].

<p>The left column represents expert-labeled bundles, the middle column depicts bundles obtained using our approach, and the right column shows bundles acquired using the Guevara et al.’s method. Our approach obtained more accurate labeling results than Guevara et al.’s method [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133337#pone.0133337.ref033" target="_blank">33</a>].</p

Major anatomic bundle list.

<p>Major anatomic bundle list.</p

Sensitivity and FDR histograms for tract group labeling, direct tract labeling, and Guevara’s method.

<p>Top left: sensitivities of the three methods for each anatomic bundle, Top right: sensitivities of the three methods for each example subject. Bottom left: FDRs of the three methods for each anatomic bundle, Bottom right: FDRs of the three methods for each example subject.</p