Nerve Segmentation of Ultrasound Images Bayesian U-Net Models

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Taryn Michael, Ibidun Christiana Obagbuwa
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Abstract

Ultrasound imaging is a widely adopted method for noninvasive 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 to the 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 article focused on developing and implementing an optimal Bayesian U-Net for nerve segmentation in ultrasound images, presented through a user-friendly application. 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 and offers a comprehensive solution that combines advanced methodology with user-friendly accessibility.

Abstract Image

超声图像神经分割的贝叶斯U-Net模型
超声成像是一种广泛采用的无创内部结构检查方法,因其成本效益、实时成像能力和无电离辐射而受到重视。它的应用,包括周围神经阻滞(PNB)程序,受益于神经结构的直接可视化。然而,超声图像固有的畸变,由回声扰动和斑点噪声引起,对神经结构的准确定位提出了挑战,即使是经验丰富的从业者。计算技术,特别是贝叶斯推理,通过在模型预测中提供不确定性估计,提供了一个有希望的解决方案。本文的重点是开发和实现最优贝叶斯U-Net神经分割超声图像,通过一个用户友好的应用程序提出。贝叶斯卷积层和蒙特卡罗dropout方法是探索和比较的两种贝叶斯技术,特别强调促进医疗专业人员的决策过程。研究表明,将蒙特卡罗退出技术集成到贝叶斯推理中可以得到最优的结果。当与典型的U-Net进行基准测试时,贝叶斯模型的平均二进制精度为98.99%,平均骰子系数得分为0.72,平均IOU得分为0.57。这项工作的高潮是为医疗专业人员实际使用而设计的应用程序,为超声图像中的贝叶斯神经分割提供了直观的界面。这项研究有助于更广泛地理解医学成像模型中的贝叶斯技术,并提供了一个综合的解决方案,将先进的方法与用户友好的可访问性相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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