Video-based multi-target multi-camera tracking for postoperative phase recognition.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 12, 2025

Abstract

PURPOSE: Deep learning methods are commonly used to generate context understanding to support surgeons and medical professionals. By expanding the current focus beyond the operating room (OR) to postoperative workflows, new forms of assistance are possible. In this article, we propose a novel multi-target multi-camera tracking (MTMCT) architecture for postoperative phase recognition, location tracking, and automatic timestamp generation.

Authors

  • Franziska Jurosch
    TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Technical University of Munich, Munich, Germany.
  • Janik Zeller
    Technical University of Munich, School of Medicine and Health, TUM University Hospital, Research Group MITI, Munich, Germany.
  • Lars Wagner
    Department of Pediatrics, Division of Pediatric Hematology and Oncology, Duke University School of Medicine, Durham, NC.
  • Ege Özsoy
    Technical University Munich, Germany.
  • Alissa Jell
    Department of Surgery, Research Group Minimally Invasive Interdisciplinary Therapeutical Intervention (MITI), Klinikum rechts der Isar, Technical University Munich (TUM), Munich, Germany, Department of Surgery, Klinikum rechts der Isar, Technical University Munich (TUM), Munich, Germany.
  • Sven Kolb
    Research Group MITI, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany.
  • Dirk Wilhelm
    Department of Surgery, Research Group Minimally Invasive Interdisciplinary Therapeutical Intervention (MITI), Klinikum rechts der Isar, Technical University Munich (TUM), Munich, Germany, Department of Surgery, Klinikum rechts der Isar, Technical University Munich (TUM), Munich, Germany.

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