FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 1, 2020

Abstract

PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos.

Authors

  • Sophia Bano
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, London, UK. sophia.bano@ucl.ac.uk.
  • Francisco Vasconcelos
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London 43-45 Foley St, London, W1W 7TY, UK.; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
  • Emmanuel Vander Poorten
    Department of Mechanical Engineering, University of Leuven, Celestijnenlaan 300B, 3001, Heverlee, Belgium.
  • Tom Vercauteren
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Sébastien Ourselin
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Jan Deprest
  • Danail Stoyanov
    University College London, London, UK.

Keywords

External Resources

Popular Topics
Recent Journals