4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome  

作　　者：Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap 

机构地区：[1]Chongqing Key Laborotory of Image Rocognition,Chongqing University of Posts and Telecommunications,Chongqing,China [2]School of Computing and Data Engineering,NingboTech University,Ningbo,China [3]Department of Bioengineering,Imperial College London,London,UK [4]Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications),Ministry of Education,Chongqing,China [5]School of Computer Science,Chongqing University,Chongqing,China [6]University of Oxford,Oxford,UK [7]Cardiovascular Research Centre,Royal Brompton Hospital,London,UK [8]National Heart and Lung Institute,Imperial College London,London,UK

出　　处：《CAAI Transactions on Intelligence Technology》2024年第6期1485-1499,共15页智能技术学报(英文)

基　　金：Natural Science Foundation of Chongqing Municipality,Grant/Award Numbers:2023NSCQLZX0045,CSTB2022NSCQ-MSX0436 and cstc202;China Postdoctoral Science Foundation,Grant/Award Number:2023M740741;National Natural Science Foundation of China,Grant/Award Numbers:62331008,62176027,62027827,62221005 and 622760;Natural Science Foundation of Ningbo Municipality,Grant/Award Number:2023J280;Zhejiang Province Postdoctoral Research Funding Project,Grant/Award Number:ZJ2023008;Ningbo Key R&D Program,Grant/Award Number:2023Z231。

摘　　要：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 coarseto-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.

关 键 词：4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]