{"title":"Nerve Segmentation of Ultrasound Images Bayesian U-Net Models","authors":"Taryn Michael, Ibidun Christiana Obagbuwa","doi":"10.1155/int/6114741","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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.</p>\n </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2024 1","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6114741","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/int/6114741","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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.