Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

In Situ Probing of Early Stage Phase Transformation of KCl in Aqueous Solutions via Liquid-Cell TEM.

Nano letters·2026

Same author

Ligand-controlled engineering of Cu-H active sites on Cu<sub>25</sub> hydride nanoclusters for efficient CO<sub>2</sub> electroreduction.

Nanoscale·2026

Same author

Coverage Optimization Strategy for Wireless Sensor Networks Based on Improved Northern Goshawk Optimization Algorithm.

Biomimetics (Basel, Switzerland)·2026

Same author

The Rise in Carbapenem-Resistant <i>Acinetobacter baumannii</i> and the Emergence of Eravacycline as a Treatment Strategy: A Narrative Review.

Pathogens (Basel, Switzerland)·2026

Same author

Microring resonator-assisted Fourier transform spectrometer using thin-film lithium niobate.

Optics express·2026

Same author

Atomic-Level Composition Engineering in M<sub>25</sub> Nanoclusters for Photocatalytic Degradation of Methylene Blue: From Homometallic Au<sub>25</sub>, Ag<sub>25</sub>, and Cu<sub>25</sub> to Heterometallic Au<sub>1</sub>Cu<sub>24</sub>.

The journal of physical chemistry. A·2026

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A parallel network utilizing local features and global representations for segmentation of surgical instruments.

Xinan Sun^1,2, Yuelin Zou^1,2, Shuxin Wang^1,2

¹Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China.

International Journal of Computer Assisted Radiology and Surgery

|June 10, 2022

Summary

This study introduces a new Mask R-CNN based method for segmenting surgical instruments in robot-assisted surgery. The approach enhances surgeon awareness by accurately identifying instrument types and parts, improving automation potential.

Keywords:

Global attention Robot-assisted surgery Surgical instrument Surgical instrument segmentation Swin-transformer

More Related Videos

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Related Experiment Videos

Last Updated: Sep 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Area of Science:

Medical Robotics
Computer Vision
Surgical Technology

Background:

Accurate segmentation of surgical instruments is crucial for enhancing surgeon awareness in robot-assisted minimally invasive surgery.
Existing methods may lack the ability to effectively capture both local details and global context of instruments.

Purpose of the Study:

To propose a novel instance segmentation method for surgical instruments using Mask R-CNN.
To improve the accuracy and generalization ability of surgical instrument segmentation.

Main Methods:

A new feature extraction backbone combining convolutional neural network (CNN) and Swin-Transformer branches was developed.
Skip fusions were implemented in the backbone to integrate local and global features effectively.
The method was evaluated on the MICCAI 2017 EndoVis Challenge dataset.

Main Results:

The proposed method achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 0.5873 for type segmentation and 0.7408 for part segmentation.
Ablation studies indicated that the novel backbone improved mIoU by at least 17%.
The method demonstrated precise segmentation of surgical instrument contours, particularly at the end tips.

Conclusions:

The novel method effectively extracts both local and global features for improved surgical instrument segmentation accuracy.
Enhanced segmentation precision contributes to more accurate localization and pose estimation of surgical instruments.
This advancement supports the further automation of robot-assisted minimally invasive surgery.