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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

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Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

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Improving neck ultrasound image retrieval using intra-sweep representation learning.

Wanwen Chen¹, Adam Schmidt^2,3, Eitan Prisman⁴

¹Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada. wanwenc@ece.ubc.ca.

International Journal of Computer Assisted Radiology and Surgery

|May 10, 2025

Summary

This study introduces a new self-supervised contrastive learning method for ultrasound (US) image retrieval in transoral robotic surgery (TORS). The approach enhances US probe localization and registration, achieving high accuracy even with tissue deformation.

Keywords:

Contrastive learning Image retrieval Transoral robotic surgery US guidance

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Area of Science:

Robotics
Medical Imaging
Computer Vision

Background:

Intraoperative ultrasound (US) enhances visualization and safety in transoral robotic surgery (TORS).
Accurate US probe localization and registration with preoperative images are crucial for US-guided TORS.
Image retrieval offers a unified framework for these tasks, requiring discriminative US representations.

Purpose of the Study:

To develop a self-supervised contrastive learning approach for matching intraoperative US images to a preoperative database.
To enhance US feature encoding using intra-sweep similarity and US probe location.
To create a flexible system for US probe localization and image registration in TORS.

Main Methods:

Proposed a self-supervised contrastive learning strategy for US image retrieval.
Incorporated intra-sweep similarity and US probe location into feature encoding.
Implemented a flexible threshold to discard inadequate matches.

Main Results:

Achieved 92.30% retrieval accuracy on simulated data, outperforming existing methods.
Demonstrated feasibility on real patient data with US-CT registration.
Showcased successful US probe localization despite tissue deformation from tongue retraction.

Conclusions:

The contrastive learning method effectively enhances US image representation learning.
The image retrieval system enables accurate neck US localization post-tongue retraction.
This approach improves the safety and precision of US-guided TORS.