摘要
胸腔积液是许多重大疾病的并发症,有创的穿刺并生化检验是确定积液性质的金标准,因此一种无创的胸腔积液性质分析模型具有重大意义。提出了基于胸腔积液超声图像标准化方法的胸腔积液性质分析模型(Property Analysis Model of Pleural Effusion,PAMPE),它能无创、快速地对积液颜色、浑浊程度和李凡特试验3种实验室指标进行分类。构造PAMPE主要分为图像标准化、构建特征工程和特征筛选并使用v-SVM构建PAMPE三大步骤。提出了胸腔积液超声图像标准化方法(Standardization Of Pleural Effusion Ultrasonic Image,SOPEU)作为模型构建过程中的图像标准化方法,它削弱了超声设备参数不同、患者肥胖程度不同和胸腔积液被骨骼与膈肌遮蔽程度不同造成的图像集中图像的灰度和尺度差异。PAMPE在多种评价指标——准确率、查准率、查全率、F1-score、混淆矩阵、接收者操作特性(Receiver Operating Characteristic,ROC)曲线和ROC曲线下的面积(Area Under ROC Curve,AUC)中均表现良好,具体来说,模型在3个分类问题上的准确率分别达到0.800,0.743和0.719,查准率分别达到0.806,0.779和0.741,查全率分别达到0.921,0.815和0.893,F1-score分别达到0.860,0.796和0.809,AUC分别达到0.820,0.700和0.709,这些指标从多个角度体现出PAMPE的有效性。对比实验表明,对于这3个分类问题,PAMPE相比不使用SOPEU构造的模型分别增加了0.090,0.048和0.086的准确率。实验结果表明标准化后的图像有效地减小了数据来源不同所导致的分类误差。
Pleural effusion is a complication of many major diseases.Invasive puncture and biochemical tests are the gold standard for diagnoising the property of pleural effusion.Therefore,a non-invasive pleural effusion analysis method is of great significance.A model based on standardization of pleural effusion ultrasonic image—Property analysis method of pleural effusion(PAMPE)is proposed.PAMPE can quickly and noninvasively classify three laboratory indexes:effusion color,effusion turbidity and Rivalta test.The construction of PAMPE is mainly divided into three steps:image standardization,construction of feature engineering and using v-SVM to build PAMPE after feature selection.In the image standardization step,a new standardization method—Standardi-zation of Pleural Effusion Ultrasonic Image(SOPEU)is also proposed.SOPEU suppresses the differences in the grayscale and scale of the images in the image set caused by the different parameters of ultrasound equipment,the different degree of obesity of patients,and the different degree to which pleural effusion is shielded by the bones and diaphragm.Experiment results illustrate that,PAMPE behaves well in a variety of evaluation indicators:accuracy,precision,recall,F1-score,confusion matrix,receiver operating characteristic(ROC)curve and area under ROC curve(AUC).Specifically,for the three classification problems,the accuracy can reach 0.800,0.743 and 0.719,the precision can reach 0.806,0.779 and 0.741,the recall can reach 0.921,0.815 and 0.893,the F1-score can reach 0.860,0.796 and 0.809 and the AUC can reach 0.820,0.700 and 0.709,which proves the effectiveness of PAMPE from different aspects.Comparative results shows that for the three classification problems,PAMPE has increased the accuracy of 0.090,0.048 and 0.086 respectively compared with the model constructed without SOPEU.The experimental results show that the normalized images effectively reduce the classification errors caused by the different quality of data sources.
作者
冯亦凡
徐琪
曾卫明
FENG Yi-fan;XU Qi;ZENG Wei-ming(Department of Computer Science,College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S01期44-53,共10页
Computer Science
基金
国家自然科学基金(31870979)
长征医院人才建设三年行动计划——“金字塔人才工程”军事医学人才项目。
关键词
胸腔积液超声图像
无创性质分析
超声图像标准化
Ultrasound image of pleural effusion
Non-invasive qualitative analysis
Ultrasound image standardization
