"Deep-Onto" network for surgical workflow and context recognition.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Surgical workflow recognition and context-aware systems could allow better decision making and surgical planning by providing the focused information, which may eventually enhance surgical outcomes. While current developments in computer-assisted surgical systems are mostly focused on recognizing surgical phases, they lack recognition of surgical workflow sequence and other contextual element, e.g., "Instruments." Our study proposes a hybrid approach, i.e., using deep learning and knowledge representation, to facilitate recognition of the surgical workflow.

Authors

  • Hirenkumar Nakawala
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, Milan, 20133, Italy. Electronic address: hirenkumar.nakawala@polimi.it.
  • Roberto Bianchi
    Department of Urology, European Institute of Oncology (IEO), Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.
  • Laura Erica Pescatori
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
  • Ottavio De Cobelli
    Department of Urology, European Institute of Oncology (IEO), Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.
  • Giancarlo Ferrigno
  • Elena De Momi