Abstract
AbstractDiffusion-weighted imaging and tractography offer a unique approach to probe the microarchitecture of brain tissue noninvasively. Whole brain tractography, however, produces an unstructured set of fiber trajectories, whereas clinical applications often demand targeted tracking of specific bundles. This work presents a novel, hybrid approach to fiber bundle segmentation, using spectral embedding and supervised learning. Training data of 20 healthy subjects is labeled with a parcellation-based method, and used to train support vector machine and random forest classifiers. Cross-validation was used to avoid overfitting. Results on testing data of five independent subjects show a clear improvement over unsupervised methods. Moreover, estimating the label probabilities allows to reduce the effect of outliers.KeywordsSupport Vector MachineRandom ForestRandom Forest ClassifierSupervise ClassifierCingulum BundleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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