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 , Yuanyuan Xu , Jian Xu , Weiping Ding , Jinzhao Yang , Huiyu Zhou , Yiming Du , Bin Hu , Lichi Zhang , 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 , Yuanyuan Xu , Jian Xu , Weiping Ding , Jinzhao Yang , Huiyu Zhou , Yiming Du , Bin Hu , Lichi Zhang , 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}
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.
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.