Abstract:
Ultrasound imaging is a widely adopted method for non-invasive examination of
internal structures, valued for its cost-effectiveness, real-time imaging capability,
and absence of ionizing radiation. Its applications, including Peripheral Nerve
Blocking (PNB) procedures, benefit from the direct visualization of nerve structures.
However, the inherent distortions in ultrasound images, arising from echo
perturbations and speckle noise, pose challenges for accurate localization of nerve
structures even for experienced practitioners. Computational techniques, particularly
Bayesian inference, offer a promising solution by providing uncertainty estimates
in model predictions.
This study focused on the development and implementation of an optimal Bayesian
U-net for nerve segmentation in ultrasound images, presented through a user-friendly
application. Utilizing Bayesian Convolution layers, and the Monte Carlo Dropout
method, were the two Bayesian techniques explored and compared, with a specific
emphasis on facilitating medical professionals’ decision-making processes. The research
revealed that integrating the Monte Carlo Dropout technique for Bayesian
inference yields the most optimal results. The Bayesian model demonstrates an average
binary accuracy of 98.99%, an average dice coefficient score of 0.72, and an
average IOU score of 0.57, when benchmarked against a typical U-net.
The culmination of this work is an application designed for practical use by medical
professionals, providing an intuitive interface for Bayesian nerve segmentation
in ultrasound images. This research contributes to the broader understanding of
Bayesian techniques in medical imaging models, offering a comprehensive solution
that combines advanced methodology with user-friendly accessibility.
