Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng
{"title":"ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation","authors":"Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng","doi":"10.1016/j.jksuci.2024.102203","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate segmentation of lung nodules is crucial for the early detection of lung cancer and other pulmonary diseases. Traditional segmentation methods face several challenges, such as the overlap between nodules and surrounding anatomical structures like blood vessels and bronchi, as well as the variability in nodule size and shape, which complicates the segmentation algorithms. Existing methods often inadequately address these issues, highlighting the need for a more effective solution. To address these challenges, this paper proposes an improved multi-scale parallel fusion encoding network, ParaU-Net. ParaU-Net enhances the segmentation accuracy and model performance by optimizing the encoding process, improving feature extraction, preserving down-sampling information, and expanding the receptive field. Specifically, the multi-scale parallel fusion mechanism introduced in ParaU-Net better captures the fine features of nodules and reduces interference from other structures. Experiments conducted on the LIDC (The Lung Image Database Consortium) public dataset demonstrate the excellent performance of ParaU-Net in segmentation tasks, with results showing an IoU of 87.15%, Dice of 92.16%, F1-score of 92.24%, F2-score of 92.33%, and F0.5-score of 92.69%. These results significantly outperform other advanced segmentation methods, validating the effectiveness and accuracy of the proposed model in lung nodule CT image analysis. The code is available at <span><span>https://github.com/XiaoBai-Lyq/ParaU-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1319157824002921","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate segmentation of lung nodules is crucial for the early detection of lung cancer and other pulmonary diseases. Traditional segmentation methods face several challenges, such as the overlap between nodules and surrounding anatomical structures like blood vessels and bronchi, as well as the variability in nodule size and shape, which complicates the segmentation algorithms. Existing methods often inadequately address these issues, highlighting the need for a more effective solution. To address these challenges, this paper proposes an improved multi-scale parallel fusion encoding network, ParaU-Net. ParaU-Net enhances the segmentation accuracy and model performance by optimizing the encoding process, improving feature extraction, preserving down-sampling information, and expanding the receptive field. Specifically, the multi-scale parallel fusion mechanism introduced in ParaU-Net better captures the fine features of nodules and reduces interference from other structures. Experiments conducted on the LIDC (The Lung Image Database Consortium) public dataset demonstrate the excellent performance of ParaU-Net in segmentation tasks, with results showing an IoU of 87.15%, Dice of 92.16%, F1-score of 92.24%, F2-score of 92.33%, and F0.5-score of 92.69%. These results significantly outperform other advanced segmentation methods, validating the effectiveness and accuracy of the proposed model in lung nodule CT image analysis. The code is available at https://github.com/XiaoBai-Lyq/ParaU-Net.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.