TRAFIC: fiber tract classification using deep learning

Prince D. Ngattai Lam; Gaetan Belhomme; Jessica Ferrall; Billie Patterson; Martin Styner; Juan C. Prieto

doi:10.1117/12.2293931

2 March 2018 TRAFIC: fiber tract classification using deep learning

Prince D. Ngattai Lam, Gaetan Belhomme, Jessica Ferrall, Billie Patterson, Martin Styner, Juan C. Prieto

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057412 (2018) https://doi.org/10.1117/12.2293931
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Prince D. Ngattai Lam, Gaetan Belhomme, Jessica Ferrall, Billie Patterson, Martin Styner, and Juan C. Prieto "TRAFIC: fiber tract classification using deep learning", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057412 (2 March 2018); https://doi.org/10.1117/12.2293931

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