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Related Experiment Video

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DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for

Li-An Tseng1, Yuan-Chih Tsai2, Meng-Yi Bai3,4

  • 1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Diagnostics (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

DeepVinci, a novel deep learning network, accurately identifies organs during robotic gynecological surgery. This automated organ identification is crucial for advancing surgical navigation systems.

Keywords:
artificial intelligenceda Vinci Robotdeep learninggynecological surgeryorgan semantic segmentation

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Area of Science:

  • Robotics in Surgery
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Automated surgical navigation involves organ identification, surgical planning, and path determination.
  • The da Vinci surgical system offers a platform for automated surgical navigation.
  • This study addresses the initial stage: organ identification in gynecological surgery.

Purpose of the Study:

  • To develop an automated method for organ identification in gynecological surgery using the da Vinci system.
  • To propose a novel deep learning network for pixel-level organ semantic segmentation.

Main Methods:

  • A convolutional neural network (CNN)-based encoder-decoder network named DeepVinci was developed.
  • A densely multi-scale pyramid module and feature fusion module were integrated to enhance global context and address limited field of view.
  • An edge supervision network was incorporated for refining segmentation results.

Main Results:

  • DeepVinci achieved state-of-the-art accuracy in organ semantic segmentation.
  • The network obtained a Dice Similarity Coefficient of 0.684 and a Mean Pixel Accuracy of 0.700.
  • Experimental results demonstrate the network's effectiveness.

Conclusions:

  • The DeepVinci network provides a practical and competitive solution for semantic segmentation in da Vinci gynecological surgery.
  • This approach advances automated surgical navigation by improving organ identification accuracy.
  • The developed method holds potential for enhancing robotic surgery systems.