Abstract #4804

Automated Segmentation of Thalamic Nuclei using Convolutional Neural Networks

Mohammad Sadegh Majdi¹, Mahesh Bharath Keerthivasan², Natalie M Zahr³, Jeffrey J Rodriguez¹, and Manoj Saranathan⁴

¹electrical and computer engineering, university of arizona, tucson, AZ, United States, ²university of arizona, tucson, AZ, United States, ³stanford, stanford, CA, United States, ⁴medical imaging, university of arizona, tucson, AZ, United States

parcellation of thalamic nuclei is critical step in targeting for deep brain surgery, volumetry for longitudinal tracking of diseases such as Alzheimer’s and multiple sclerosis,. However, thalamic nuclei are mostly indistinguishable in conventional T1 or T2 weighted MRI. In this study, we propose a deep neural network based method to achieve a fast and accurate segmentation of thalamic nuclei, taking advantage of high contrast characteristics of a white matter nulled MPRAGE sequence at both 3T and 7T.

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