Deep learning approaches to surgical video segmentation and object detection: A scoping review.

Journal: Computers in biology and medicine
Published Date: Jun 2, 2025

Abstract

INTRODUCTION: Computer vision (CV) has had a transformative impact in biomedical fields such as radiology, dermatology, and pathology. Its real-world adoption in surgical applications, however, remains limited. We review the current state-of-the-art performance of deep learning (DL)-based CV models for segmentation and object detection of anatomical structures in videos obtained during surgical procedures.

Authors

  • Devanish N Kamtam
    Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: devanish@stanford.edu.
  • Joseph B Shrager
    Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
  • Satya Deepya Malla
    Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Nicole Lin
    Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Juan J Cardona
    Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Jake J Kim
    Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Clarence Hu
    Hotpot.ai, Palo Alto, CA, USA.

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