Measuring proximity to standard planes during fetal brain ultrasound
  scanning
Chiara Di Vece Antonio Cirigliano Meala Le Lous Raffaele Napolitano Anna L. David Donald Peebles Pierre Jannin Francisco Vasconcelos Danail Stoyanov
This paper introduces a novel pipeline designed to bring ultrasound (US)
plane pose estimation closer to clinical use for more effective navigation to
the standard planes (SPs) in the fetal brain. We propose a semi-supervised
segmentation model utilizing both labeled SPs and unlabeled 3D US volume
slices. Our model enables reliable segmentation across a diverse set of fetal
brain images. Furthermore, the model incorporates a classification mechanism to
identify the fetal brain precisely. Our model not only filters out frames
lacking the brain but also generates masks for those containing it, enhancing
the relevance of plane pose regression in clinical settings. We focus on fetal
brain navigation from 2D ultrasound (US) video analysis and combine this model
with a US plane pose regression network to provide sensorless proximity
detection to SPs and non-SPs planes; we emphasize the importance of proximity
detection to SPs for guiding sonographers, offering a substantial advantage
over traditional methods by allowing earlier and more precise adjustments
during scanning. We demonstrate the practical applicability of our approach
through validation on real fetal scan videos obtained from sonographers of
varying expertise levels. Our findings demonstrate the potential of our
approach to complement existing fetal US technologies and advance prenatal
diagnostic practices.