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Abstract #2441

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

Brian McCrindle1,2, Nicholas Simard1,2, Ethan Samson2,3, Ethan Danielli 2,3, Thomas E. Doyle1,3,4, and Michael D. Noseworthy1,2,3
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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