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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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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

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Can uncertainty estimation predict segmentation performance in ultrasound bone imaging?

Prashant U Pandey¹, Pierre Guy², Antony J Hodgson³

¹School of Biomedical Engineering, University of British Columbia, Vancouver, Canada. prashant@ece.ubc.ca.

International Journal of Computer Assisted Radiology and Surgery

|April 4, 2022

Summary

Ensemble learning improves bone surface segmentation in ultrasound (US) images for computer-assisted surgery. However, current uncertainty measures cannot reliably predict segmentation performance, limiting clinical trust.

Keywords:

Bone Segmentation Ultrasound Uncertainty Estimation

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Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

Published on: March 6, 2019

Area of Science:

Medical Imaging
Computer-Assisted Surgery
Machine Learning

Background:

Ultrasound (US) image segmentation is crucial for computer-assisted orthopaedic surgery.
Neural networks show promise for segmentation but require reliable uncertainty estimation for clinical trust.

Purpose of the Study:

To evaluate uncertainty estimation methods for neural network-based bone surface segmentation in ultrasound images.
To assess the impact of data quality on segmentation performance and uncertainty measures.

Main Methods:

Implemented a U-Net model with Monte Carlo dropout, test-time augmentation, and ensemble learning for uncertainty estimation.
Evaluated segmentation performance, calibration quality, and predictive ability on 13,687 ultrasound images.

Main Results:

Ensemble learning with binary cross-entropy (BCE) loss yielded the best segmentation performance (Dice: 0.75-0.78) and calibration (mean error: 0.22-0.28%).
Uncertainty measures were not reliable predictors of surface segmentation performance, unlike in area/volumetric segmentation.

Conclusions:

Ensemble learning and BCE loss significantly enhance US segmentation performance and calibration.
Current uncertainty estimation techniques do not reliably predict future segmentation performance in this context.