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Automated angular measurement for puncture angle using a computer-aided method in ultrasound-guided peripheral

Haruyuki Watanabe¹, Hironori Fukuda², Yuina Ezawa³

¹School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Japan. hal-watanabe@gchs.ac.jp.

Physical and Engineering Sciences in Medicine

|February 15, 2024

Summary

This study introduces a novel method combining deep learning and image processing to accurately measure needle puncture angles during ultrasound-guided vascular access. This technique enhances precision and reduces procedure time for medical professionals.

Keywords:

Angular measurements Deep learning Puncture angle U-Net Ultrasound

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Area of Science:

Medical Imaging
Ultrasound Technology
Image Processing

Background:

Ultrasound guidance is standard for vascular access, but precise needle angle measurement is crucial for success.
Current deep learning methods improve needle visibility but often neglect puncture angle calculation.
Accurate angle determination is essential for optimizing needle trajectory and improving procedural outcomes.

Purpose of the Study:

To develop and validate a method for detecting target vessels and needles using combined deep learning and image processing.
To automatically derive the puncture angle for ultrasound-guided vascular access.
To improve the accuracy and efficiency of angle measurements compared to manual methods.

Main Methods:

Utilized the U-Net architecture for segmenting median cubital veins, simulated vessels, and needles from ultrasound images.
Employed conventional image processing techniques, including the Hough transform, for automated angle measurement.
Collected data from 20 healthy volunteers and four phantoms simulating vascular access procedures.

Main Results:

Achieved high segmentation accuracy with mean Dice coefficients of 0.826 for veins, 0.931 for simulated vessels, and 0.773 for needles.
Demonstrated strong agreement between expert and automated puncture angle measurements, with a correlation of 0.847.
The automated system significantly reduced variability and time compared to manual angle measurements.

Conclusions:

The proposed hybrid approach offers high segmentation accuracy and reliable automated puncture angle measurement.
This method has the potential to enhance operator focus on critical procedural techniques by automating angle calculations.
The findings suggest a significant improvement in the efficiency and precision of ultrasound-guided vascular access procedures.