Ngattai Lam Prince D, Belhomme Gaetan, Ferrall Jessica, Patterson Billie, Styner Martin, Prieto Juan C
NIRAL, UNC, Chapel Hill, North Carolina, United States.
Proc SPIE Int Soc Opt Eng. 2018 Feb;10574. doi: 10.1117/12.2293931. Epub 2018 Mar 2.
We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.
我们展示了TRAFIC，这是一种用于脑纤维束标记和分类的全自动工具。TRAFIC使用一个神经网络对新纤维进行分类，该神经网络是通过从先前追踪并手动校正的纤维束计算出的形状特征进行训练的。由于它应用于已经追踪的纤维，因此它独立于DTI图谱。这项工作的动机来自医学应用，在这些应用中，从DTI图谱中提取纤维或手动对纤维进行分类的过程既耗时又需要有关脑解剖学的知识。通过这种新方法，我们能够对追踪的纤维束进行分类并获得令人鼓舞的结果。在本报告中，我们将详细介绍所使用的方法以及通过我们的方法所取得的结果。
