基于深度内容和对比感知学习的胎儿颈部半透明图像质量自动评估

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Lili Zhao , Yuanyuan Xu , Jian Xu , Weiping Ding , Jinzhao Yang , Huiyu Zhou , Yiming Du , Bin Hu , Lichi Zhang , Qian Wang
{"title":"基于深度内容和对比感知学习的胎儿颈部半透明图像质量自动评估","authors":"Lili Zhao ,&nbsp;Yuanyuan Xu ,&nbsp;Jian Xu ,&nbsp;Weiping Ding ,&nbsp;Jinzhao Yang ,&nbsp;Huiyu Zhou ,&nbsp;Yiming Du ,&nbsp;Bin Hu ,&nbsp;Lichi Zhang ,&nbsp;Qian Wang","doi":"10.1016/j.engappai.2025.110687","DOIUrl":null,"url":null,"abstract":"<div><div>Automatic quality assessment of fetal nuchal translucency ultrasound images can assist physicians in obtaining standard planes and improve the reproducibility of nuchal translucency screening. At present, there are no special studies and methods for the quality assessment of fetal nuchal translucency ultrasound images. For this task, main challenges are low image quality, content identification of structural integrity and relative position relationship, time consumption for data collection and fine-grained annotation. To address these challenges, we propose a framework based on DenseNet model, which includes preprocessing module, content perception module, attention learning module and contrastive regularization module. Experiments show that the modules are effective for improving the quality assessment framework performance. And this framework is better than the other fourteen deep learning models. This framework can provide the sonographer with a model interpretable reference map. Bland–Altman experimental analysis also verifies the consistency between the results obtained by the automatic quality assessment framework and the manually annotated clinical dataset. Therefore, the proposed quality assessment framework for fetal nuchal translucency ultrasound images has the prospect and value of clinical application.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"152 ","pages":"Article 110687"},"PeriodicalIF":8.0000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Content and Contrastive Perception learning for automatic fetal nuchal translucency image quality assessment\",\"authors\":\"Lili Zhao ,&nbsp;Yuanyuan Xu ,&nbsp;Jian Xu ,&nbsp;Weiping Ding ,&nbsp;Jinzhao Yang ,&nbsp;Huiyu Zhou ,&nbsp;Yiming Du ,&nbsp;Bin Hu ,&nbsp;Lichi Zhang ,&nbsp;Qian Wang\",\"doi\":\"10.1016/j.engappai.2025.110687\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Automatic quality assessment of fetal nuchal translucency ultrasound images can assist physicians in obtaining standard planes and improve the reproducibility of nuchal translucency screening. At present, there are no special studies and methods for the quality assessment of fetal nuchal translucency ultrasound images. For this task, main challenges are low image quality, content identification of structural integrity and relative position relationship, time consumption for data collection and fine-grained annotation. To address these challenges, we propose a framework based on DenseNet model, which includes preprocessing module, content perception module, attention learning module and contrastive regularization module. Experiments show that the modules are effective for improving the quality assessment framework performance. And this framework is better than the other fourteen deep learning models. This framework can provide the sonographer with a model interpretable reference map. Bland–Altman experimental analysis also verifies the consistency between the results obtained by the automatic quality assessment framework and the manually annotated clinical dataset. Therefore, the proposed quality assessment framework for fetal nuchal translucency ultrasound images has the prospect and value of clinical application.</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"152 \",\"pages\":\"Article 110687\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197625006876\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197625006876","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

胎儿颈部半透明超声图像的自动质量评估可以帮助医生获得标准平面,提高颈部半透明筛查的可重复性。目前,对胎儿颈部半透明超声图像的质量评价尚无专门的研究和方法。对于该任务,主要挑战是图像质量低、结构完整性和相对位置关系的内容识别、数据收集耗时和细粒度标注。为了解决这些问题,我们提出了一个基于DenseNet模型的框架,该框架包括预处理模块、内容感知模块、注意学习模块和对比正则化模块。实验表明,这些模块能够有效地提高质量评估框架的性能。这个框架比其他14个深度学习模型要好。这个框架可以为超声医师提供一个模型可解释的参考图。Bland-Altman实验分析也验证了自动质量评估框架与人工标注临床数据集结果的一致性。因此,所提出的胎儿颈部半透明超声图像质量评价框架具有临床应用前景和价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Content and Contrastive Perception learning for automatic fetal nuchal translucency image quality assessment
Automatic quality assessment of fetal nuchal translucency ultrasound images can assist physicians in obtaining standard planes and improve the reproducibility of nuchal translucency screening. At present, there are no special studies and methods for the quality assessment of fetal nuchal translucency ultrasound images. For this task, main challenges are low image quality, content identification of structural integrity and relative position relationship, time consumption for data collection and fine-grained annotation. To address these challenges, we propose a framework based on DenseNet model, which includes preprocessing module, content perception module, attention learning module and contrastive regularization module. Experiments show that the modules are effective for improving the quality assessment framework performance. And this framework is better than the other fourteen deep learning models. This framework can provide the sonographer with a model interpretable reference map. Bland–Altman experimental analysis also verifies the consistency between the results obtained by the automatic quality assessment framework and the manually annotated clinical dataset. Therefore, the proposed quality assessment framework for fetal nuchal translucency ultrasound images has the prospect and value of clinical application.
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
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信