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Weakly supervised segmentation for real-time surgical tool tracking.

Eung-Joo Lee^1,2, William Plishker², Xinyang Liu³

¹Department of Electrical and Computer Engineering and the Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA.

Healthcare Technology Letters

|February 11, 2020

Summary

This summary is machine-generated.

This study introduces a weakly supervised method for surgical tool segmentation and tracking using hybrid sensors. It overcomes data annotation limitations, enabling accurate, automatic tool tracking in laparoscopic surgery.

Keywords:

annotated training data automatic tool segmentation binary segmentation mask computer vision deep learning-based methods electromagnetic tracking endoscopes image segmentation image sequences laparoscopic image processing learning (artificial intelligence)light-weight deep segmentation network medical image processing medical robotics neural nets pixel-wise training data real-time surgical tool tracking robust tool tracking supervised segmentation surgery surgical scenarios surgical tool segmentation tracking tracking robustness vision-based methods

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Area of Science:

Medical technology
Computer vision
Surgical robotics

Background:

Surgical tool tracking is crucial for various surgical applications.
Electromagnetic (EM) tracking and vision-based methods have limitations in accuracy and robustness.
Deep learning methods require extensive pixel-wise annotated data, incurring high labor costs.

Purpose of the Study:

To propose a weakly supervised method for surgical tool segmentation and tracking.
To overcome the bottleneck of manual data annotation in deep learning for surgical tools.
To enable accurate and robust surgical tool tracking in laparoscopic images.

Main Methods:

Utilized a hybrid sensor system integrating EM tracking and laparoscopic image processing.
Generated semantic labels concurrently from EM tracking and image processing.
Trained a light-weight deep segmentation network for binary mask generation.
Enabled tool tracking via the generated segmentation mask.

Main Results:

Achieved accurate, automatic surgical tool segmentation without manual labeling.
Demonstrated robust tool tracking in laparoscopic image sequences.
The proposed method is the first to integrate EM tracking and laparoscopic image processing for training label generation.

Conclusions:

The weakly supervised approach effectively addresses the challenge of limited annotated data.
The hybrid sensor system enables accurate and robust surgical tool segmentation and tracking.
This method has significant potential for improving computer-assisted and robotic surgery.