Abstract #2441

Microstructural White Matter Segmentation in Mild Traumatic Brain Injury Patients using DTI and a Deep 2D-UNet Ensemble

Brian McCrindle^1,2, Nicholas Simard^1,2, Ethan Samson^2,3, Ethan Danielli ^2,3, Thomas E. Doyle^1,3,4, and Michael D. Noseworthy^1,2,3

¹Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, ²Imaging Research Center, St. Joseph's Healthcare, Hamilton, ON, Canada, ³School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada, ⁴Vector Institute, Toronto, ON, Canada

Patients who experience a mild traumatic brain injury often suffer from microstructural white matter damage that even radiologists are unable to detect. By employing diffusion tensor imaging and a deep 2D-UNet ensemble network, we developed an image processing pipeline capable of detecting and segmenting damaged white matter regions. We show that ensemble networks are more reliable compared to any single model over the prediction threshold range under test-time-augmentation.

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