Automatic Segmentation for Analysis of Murine Cardiac Ultrasound and Photoacoustic Image Data Using Deep Learning.

Journal: Ultrasound in medicine & biology
PMID: 38825556

Abstract

OBJECTIVE: Although there are methods to identify regions of interest (ROIs) from echocardiographic images of myocardial tissue, they are often time-consuming and difficult to create when image quality is poor. Further, while myocardial strain from ultrasound (US) images can be estimated, US alone cannot obtain functional information, such as oxygen saturation (sO). Photoacoustic (PA) imaging, however, can be used to quantify sO levels within tissue non-invasively.

Authors

  • Katherine A Leyba
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.
  • Hayley Chan
    Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Olivia Loesch
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.
  • Salomé Belec
    PhyMedExp, IPAM/Biocampus, INSERM, CNRS, Université de Montpellier, Montpellier, France.
  • Pierre Sicard
    PhyMedExp, IPAM/Biocampus, INSERM, CNRS, Université de Montpellier, Montpellier, France.
  • Craig J Goergen
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA. Electronic address: cgoergen@purdue.edu.

Keywords

External Resources

Popular Topics
Recent Journals