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Pavan Annangi, Sigmund Frigstad, S B Subin

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Summary

Automated ultrasound tools can help measure residual bladder volume, aiding patients with urinary retention. This study presents a segmentation algorithm for accurate bladder volume measurement using machine learning and active contours.

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Area of Science:

Medical Imaging
Biomedical Engineering
Urology

Background:

Residual bladder volume (RBV) measurement is crucial for managing urinary retention.
Handheld ultrasound devices offer potential for convenient bedside and outpatient monitoring.
Non-expert users require automated tools to enhance usability of ultrasound devices.

Purpose of the Study:

To develop a robust segmentation algorithm for automated bladder volume measurement.
To aid non-traditional users (nurses, general physicians) in using handheld ultrasound devices.
To improve the accuracy and efficiency of residual bladder volume assessment.

Main Methods:

A segmentation algorithm combining machine learning and active contour models was developed.
The algorithm segments bladder contours from both sagittal and transverse ultrasound views.
The method was validated on a dataset of 50 unseen images and 23 image pairs.

Main Results:

The developed algorithm demonstrated robust performance in segmenting bladder contours.
Automated measurement of bladder volume was achieved through contour segmentation.
Quantitative performance metrics were reported based on the tested image datasets.

Conclusions:

The developed segmentation algorithm shows promise for automated bladder volume measurement.
This technology can enhance the utility of handheld ultrasound devices for non-expert users.
Automated RBV measurement can improve patient monitoring in various clinical settings.