基于Swin-Unet的主动脉再缩窄预测研究  

Aortic Re-coarctation Prediction Research Based on Swin-Unet

作　　者：甘孟坤 曾安[1] 张小波[2] Gan Meng-kun;Zeng An;Zhang Xiao-bo(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China;School of Automation,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学计算机学院,广东广州510006 [2]广东工业大学自动化学院,广东广州510006

出　　处：《广东工业大学学报》2023年第5期34-40,共7页Journal of Guangdong University of Technology

基　　金：广东省重点领域研发计划项目(2021B0101220006);广东省科技计划项目(2019A050510041);广东省自然科学基金资助项目(2021A1515012300);云南省重大科技专项项目(202102AA100012)。

摘　　要：主动脉缩窄(Coarctation of Aorta,CoA)是主动脉弓部的先天性畸形,自然预后差,需要早期干预甚至急诊手术治疗,同时术后主动脉再缩窄仍是可能面临的问题。目前主动脉再缩窄的预测主要由医生基于病人的临床特征并结合超声心动图(Ultra Sound Cardiogram)数据展开风险因素分析,结果往往依赖于超声心动图片拍摄质量以及医生诊断经验,诊断误诊较多。本文以患者心脏部位电子计算机断层扫描(Computed Tomography,CT)的图像结合患者的临床数据为研究对象,提出了一种基于Swin-Unet网络的多模态数据检测框架,框架结合了Swin-Unet网络模型与机器学习模型,展开多模态特征融合分析,实现主动脉再缩窄的早期检测。临床数据集实验结果显示,与传统的仅使用临床数据的预测方法相比,本文方法有效提升了主动脉再缩窄预测的效果,并验证了与再缩窄有关的风险因素,可为临床医学提供参考。Coarctation of aorta(CoA)is a congenital malformation of the aortic arch with a poor natural prognosis,which requires early intervention and even emergency surgery.Meanwhile,postoperative aortic re-coarctation is still a possible problem.At present,the prediction of aortic re-coarctation is mainly carried out based on the risk factor analysis of doctors on the clinical characteristics of patients combining with echocardiography(Ultra Sound Cardiogram)data,which is easy to be misdiagnosed.In this paper,a multimodal data detection framework based on Swin-Unet network is proposed based on the images of the patient's heart from computed tomography(CT)combining with the patient's clinical data.The framework carries out multimodal feature fusion analysis by combining the Swin-Unet network and the machine learning models,aiming to perform early detection of aortic re-coarctation.The experimental results on the clinical dataset show that our proposed methodeffectively improves the prediction effect of aortic re-coarctation when compared with the traditional prediction methods using clinical data.Particularly,we verifie the risk factors related to re-coarctation,the results of which provides a reference for clinical medicine.

关 键 词：主动脉缩窄 多模态特征融合 图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]