Neurocognitive function is often associated with structural differences in the brain for patients with neurofibromatosis type-1 (NF1), and studies have shown that NF1 is associated with larger subcortical volumes and thicker cortices of certain brain structures. Routine monitoring of NF1 patients would be possible with tools that enable rapid whole-brain segmentation in standard of care T1w MRI. Modern machine learning techniques, including fully convolutional networks (FCNs), have demonstrated the ability to rapidly perform segmentation tasks across a range of applications. In this work, we investigate the performance of different FCNs for rapid whole-brain segmentation in pediatric T1w brain MRI.
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