A 3D pose estimation framework for preterm infants hospitalized in the Neonatal Unit

Abstract

AbstractInfant pose estimation is crucial in different clinical applications, including preterm automatic general movements assessment. Recent infant pose estimation methods are limited by a lack of real clinical data and are mainly focused on 2D detection. We introduce a stereoscopic system for infants’ 3D pose estimation, based on fine-tuning state-of-the-art 2D human pose estimation networks on a large, real, and manually annotated dataset of infants’ images. Our dataset contains over 88k images, collected from 175 videos from 53 premature infants born <33 weeks of gestational age (GA), acquired within the Neonatology department of the Centre Hospitalier Universitaire de Saint Etienne, France, between 32 and 41 weeks of GA. This framework significantly reduced the pose estimation error compared to existing 2D infant pose estimation networks. It achieved a mean error of 1.72 cm on 18000 stereoscopic images in the 3D pose estimation task. This framework is the first 3D pose estimation tool dedicated to preterm infants hospitalized in the Neonatal Unit that does not depend on any visual markers or infrared cameras.