用于肺结节分割的深度学习模型:比较研究

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-09-03 DOI:10.1007/s42835-024-02032-1
Aliya Orazalina, Heechul Yoon, Sang-II Choi, Seokhyun Yoon
{"title":"用于肺结节分割的深度学习模型:比较研究","authors":"Aliya Orazalina, Heechul Yoon, Sang-II Choi, Seokhyun Yoon","doi":"10.1007/s42835-024-02032-1","DOIUrl":null,"url":null,"abstract":"<p>Lung nodule detection is clinically crucial but challenging and time-consuming. The development of automated segmentation approaches could be helpful. To assess the capability of deep learning methods for lung diagnosis, this paper compares recent deep learning models and evaluates their performance. We implemented several preprocessing steps, including windowing, thresholding, and resizing, to improve the image quality, adjust the image dimension suitable for the network, and focus on specific areas of interest within an image. The evaluation was conducted on the Lung Image Database Consortium (LIDC) dataset using Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics with model complexity parameters for a multifaceted comparison of the models. The experiments showed that the highest accuracy among the five chosen models (97.80% DSC and 1.29 HD) was reached by the Connected-UNets model, which also has the highest computational complexity. In this paper, we quantitatively evaluated and compared 5 deep learning models namely Salient Attention UNet, Connected-UNets, DDANet, UTNet, and EdgeNeXt. The evidence-based overview of current deep learning achievements for the clinical community investigated in this study can be useful to the research community in developing a new model and, thus, designing computer-aided detection and diagnosis (CAD) systems.</p>","PeriodicalId":15577,"journal":{"name":"Journal of Electrical Engineering & Technology","volume":"2016 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning Models for Lung Nodule Segmentation: A Comparative Study\",\"authors\":\"Aliya Orazalina, Heechul Yoon, Sang-II Choi, Seokhyun Yoon\",\"doi\":\"10.1007/s42835-024-02032-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Lung nodule detection is clinically crucial but challenging and time-consuming. The development of automated segmentation approaches could be helpful. To assess the capability of deep learning methods for lung diagnosis, this paper compares recent deep learning models and evaluates their performance. We implemented several preprocessing steps, including windowing, thresholding, and resizing, to improve the image quality, adjust the image dimension suitable for the network, and focus on specific areas of interest within an image. The evaluation was conducted on the Lung Image Database Consortium (LIDC) dataset using Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics with model complexity parameters for a multifaceted comparison of the models. The experiments showed that the highest accuracy among the five chosen models (97.80% DSC and 1.29 HD) was reached by the Connected-UNets model, which also has the highest computational complexity. In this paper, we quantitatively evaluated and compared 5 deep learning models namely Salient Attention UNet, Connected-UNets, DDANet, UTNet, and EdgeNeXt. The evidence-based overview of current deep learning achievements for the clinical community investigated in this study can be useful to the research community in developing a new model and, thus, designing computer-aided detection and diagnosis (CAD) systems.</p>\",\"PeriodicalId\":15577,\"journal\":{\"name\":\"Journal of Electrical Engineering & Technology\",\"volume\":\"2016 1\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrical Engineering & Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s42835-024-02032-1\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42835-024-02032-1","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

肺结节检测对临床至关重要,但具有挑战性且耗时。开发自动分割方法可能会有所帮助。为了评估深度学习方法在肺部诊断方面的能力,本文比较了最近的深度学习模型并评估了它们的性能。我们实施了几个预处理步骤,包括开窗、阈值化和调整大小,以提高图像质量,调整适合网络的图像维度,并关注图像中特定的感兴趣区域。评估在肺部图像数据库联盟(LIDC)数据集上进行,使用骰子相似系数(DSC)和豪斯多夫距离(HD)指标,并结合模型复杂度参数,对模型进行了多方面的比较。实验结果表明,在所选的五个模型中,Connected-UNets 模型的准确率最高(DSC 为 97.80%,HD 为 1.29),同时该模型的计算复杂度也最高。本文对 Salient Attention UNet、Connected-UNets、DDANet、UTNet 和 EdgeNeXt 五种深度学习模型进行了定量评估和比较。本研究对目前临床界深度学习成果的循证概述,有助于研究界开发新模型,从而设计计算机辅助检测和诊断(CAD)系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Deep Learning Models for Lung Nodule Segmentation: A Comparative Study

Lung nodule detection is clinically crucial but challenging and time-consuming. The development of automated segmentation approaches could be helpful. To assess the capability of deep learning methods for lung diagnosis, this paper compares recent deep learning models and evaluates their performance. We implemented several preprocessing steps, including windowing, thresholding, and resizing, to improve the image quality, adjust the image dimension suitable for the network, and focus on specific areas of interest within an image. The evaluation was conducted on the Lung Image Database Consortium (LIDC) dataset using Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics with model complexity parameters for a multifaceted comparison of the models. The experiments showed that the highest accuracy among the five chosen models (97.80% DSC and 1.29 HD) was reached by the Connected-UNets model, which also has the highest computational complexity. In this paper, we quantitatively evaluated and compared 5 deep learning models namely Salient Attention UNet, Connected-UNets, DDANet, UTNet, and EdgeNeXt. The evidence-based overview of current deep learning achievements for the clinical community investigated in this study can be useful to the research community in developing a new model and, thus, designing computer-aided detection and diagnosis (CAD) systems.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
期刊最新文献
Parameter Solution of Fractional Order PID Controller for Home Ventilator Based on Genetic-Ant Colony Algorithm Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN Aggregation and Bidding Strategy of Virtual Power Plant Power Management of Hybrid System Using Coronavirus Herd Immunity Optimizer Algorithm A Review on Power System Security Issues in the High Renewable Energy Penetration Environment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1