Multi-modal body part segmentation of infants using deep learning.

Journal: Biomedical engineering online
Published Date: Mar 22, 2023

Abstract

BACKGROUND: Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning signs for severe diseases such as sepsis. Thermography may be a non-contact and wireless alternative to state-of-the-art, cable-based methods. For monitoring use in clinical practice, automatic segmentation of the different body regions is necessary due to the movement of the infant.

Authors

  • Florian Voss
    Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland. voss@hia.rwth-aachen.de.
  • Noah Brechmann
    Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Deutschland.
  • Simon Lyra
    Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany.
  • Jöran Rixen
    Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany.
  • Steffen Leonhardt
  • Christoph Hoog Antink
    Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany.

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