3D Convolutional Neural Network Segmentation of White Matter Tract Masks from MR Diffusion Anisotropy Maps

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 5
• Main Authors: Pomiecko, Kristofer, Sestili, Carson, Fissell, Kate, Pathak, Sudhir, Okonkwo, David, Schneider, Walter
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: Anisotropic magnetoresistance; Convolutional Neural Network; Deep learning; Diffusion imaging; Fiber bundle segmentation; Frequency modulation; Image segmentation; Manuals; Three-dimensional displays; Training; White matter
• ISSN: 1945-8452
• DOI: 10.1109/ISBI.2019.8759575

This paper presents an application of 3D convolutional neural network (CNN) techniques to compute the white matter region spanned by a fiber tract (the tract mask) from whole-brain MRI diffusion anisotropy maps. The DeepMedic CNN platform was used, allowing for training directly on 3D volumes. The dataset consisted of 240 subjects, controls and traumatic brain injury (TBI) patients, scanned with a high angular direction and high b-value multi-shell diffusion protocol. Twelve tract masks per subject were learned. Median Dice scores of 0.72 were achieved over the 720 test masks in comparing learned tract masks to manually created masks. This work demonstrates ability to learn complex spatial regions in control and patient populations and contributes a new application of CNNs as a fast pre-selection tool in automated white matter tract segmentation methods.