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Proceedings Paper

TRAFIC: fiber tract classification using deep learning
Author(s): Prince D. Ngattai Lam; Gaetan Belhomme; Jessica Ferrall; Billie Patterson; Martin Styner; Juan C. Prieto
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Paper 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.

Paper Details

Date Published: 2 March 2018
PDF: 9 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057412 (2 March 2018); doi: 10.1117/12.2293931
Show Author Affiliations
Prince D. Ngattai Lam, The Univ. of North Carolina at Chapel Hill (United States)
Gaetan Belhomme, The Univ. of North Carolina at Chapel Hill (United States)
Jessica Ferrall, The Univ. of North Carolina at Chapel Hill (United States)
Billie Patterson, The Univ. of North Carolina at Chapel Hill (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)
Juan C. Prieto, The Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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