Abstract #4806
An Automated Pose and Motion Estimation Pipeline in Dynamic 3D Fetal MRI
Junshen Xu1, Molin Zhang1, Lana Vasung2,3,4, Esra Abaci Turk2,3,4, Borjan Gagoski3,4,5, Polina Golland1,6, P. Ellen Grant2,3,4,5, and Elfar Adalsteinsson1,7
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Department of Pediatrics, Boston Children’s Hospital, Boston, MA, United States, 3Harvard Medical School, Boston, MA, United States, 4Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 5Department of Radiology, Boston Children’s Hospital, Boston, MA, United States, 6Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 7Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
Synopsis
Fetal motion is an important indicator of fetal health and nervous system development. Current assessments of fetal motion with MRI or ultrasound are qualitative and do not reflect the 3D motion of each body part . To study the detailed motion of fetuses, annotations of fetal pose are required, which would be time-consuming through manually-labelled data for each scan. In this work, we demonstrate an automated and efficient pipeline for fetal pose and motion estimation of fetal MRI using deep learning. The results of experiments show that the proposed pipeline outperforms other state-of-the-art fetal pose estimation methods.
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