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Abstract #1896

Rapid brain segmentation in T1w MRI with fully convolutional networks: development and comparison of different network constructions

Jeremiah W Sanders1, Jason M Johnson2, Jong Bum Son1, Zijian Zhou1, Henry Szu-Meng Chen1, Joshua Yung1, Jason Szu-Meng Stafford1, Melissa Chen2, Maria Gule-Monroe2, Ho-Ling Liu1, Mark D Pagel3, and Jingfei Ma1
1Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2Neuroradiology, University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States

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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