Abstract #3551

Fetal pose estimation from volumetric MRI using generative adversarial network

Junshen Xu¹, Molin Zhang¹, Esra Turk², Polina Golland^1,3, P. Ellen Grant^2,4, and Elfar Adalsteinsson^1,5

¹Department of Electrical Engineering & Computer Science, MIT, Cambridge, MA, United States, ²Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ³Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT, Cambridge, MA, United States, ⁴Harvard Medical School, Boston, MA, United States, ⁵Institute for Medical Engineering and Science, MIT, Cambridge, MA, United States

Estimating fetal pose from 3D MRI has a wide range of applications including fetal motion tracking and prospective motion correction. Fetal pose estimation is challenging since fetuses may have different orientation and body configuration in utero. In this work, we propose a method for fetal pose estimation from low-resolution 3D EPI MRI using generative adversarial network. Results show that the proposed method produces a more robust estimation of fetal pose and achieves higher accuracy compared with conventional convolution neural network.

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