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 asAlzheimer’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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