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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