4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome
IF 8.4 2区 计算机科学Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
{"title":"4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome","authors":"Gang Wang, Weisheng Li, Mingliang Zhou, Haobo Zhu, Guang Yang, Choon Hwai Yap","doi":"10.1049/cit2.12354","DOIUrl":null,"url":null,"abstract":"<p>Hypoplastic left heart syndrome (HLHS) is a rare, complex, and incredibly foetal congenital heart disease. To decrease neonatal mortality, evolving HLHS (eHLHS) in pregnant women should be critically diagnosed as soon as possible. However, diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images, making it difficult to rapidly and easily examine for this disease. Herein, the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS (RDeH), which we have named RDeH-Net. Briefly, the framework implements a coarse-to-fine two-stage detection approach, with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains, and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician assistance. The experiments extensively compare the authors’ network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits: (1) it achieved superior average accuracy of 99.37% on three categories of foetal ultrasound images from different cases; (2) it demonstrates visually fine detection performance with weakly supervised localisation. This framework could be used to accelerate the diagnosis of eHLHS, and hence significantly lessen reliance on experienced medical physicians.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1485-1499"},"PeriodicalIF":8.4000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12354","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cit2.12354","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
Hypoplastic left heart syndrome (HLHS) is a rare, complex, and incredibly foetal congenital heart disease. To decrease neonatal mortality, evolving HLHS (eHLHS) in pregnant women should be critically diagnosed as soon as possible. However, diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images, making it difficult to rapidly and easily examine for this disease. Herein, the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS (RDeH), which we have named RDeH-Net. Briefly, the framework implements a coarse-to-fine two-stage detection approach, with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains, and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician assistance. The experiments extensively compare the authors’ network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits: (1) it achieved superior average accuracy of 99.37% on three categories of foetal ultrasound images from different cases; (2) it demonstrates visually fine detection performance with weakly supervised localisation. This framework could be used to accelerate the diagnosis of eHLHS, and hence significantly lessen reliance on experienced medical physicians.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.