Download PDFOpen PDF in browserDeep Learning-based Plane Pose Regression towards Training in Freehand Obstetric UltrasoundEasyChair Preprint 81512 pages•Date: May 31, 2022AbstractIn obstetric ultrasound (US), standard planes (SPs) retain a significant clinical relevance, but their acquisition requires the ability to mentally build a 3D map of the fetus from a 2D US image. The autonomous probe navigation towards SP remains a challenging task due to the need to interpret variable and complex images and their spatial relationship. Our work focuses on developing a real-time training platform to guide inexperienced sonographers in acquiring proper obstetric US images that could be potentially deployed for existing US machines. First, we developed a Unity-based environment for volume reconstruction and acquisition of synthetic images to this aim. Secondly, we trained a regression CNN for the 6D pose estimation of arbitrarily oriented US planes on phantom data and fine-tuned it on real ones. Keyphrases: Fetal ultrasound, deep learning, pose regression, surgical training
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