PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
Prince D. Ngattai Lam,Gaetan Belhomme,Jessica Ferrall,Billie Patterson,Martin Styner, andJuan 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
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Prince D. Ngattai Lam, Gaetan Belhomme, Jessica Ferrall, Billie Patterson, Martin Styner, 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