基于深度学习的高分辨率食管测压图谱中食管收缩活力分类

Deep Learned Esophageal Contraction Vigor Classification on High-resolution Manometry Images

高分辨率食管测压技术(HRM)作为检测食管动力障碍性疾病(EMD)的金标准,已广泛应用于临床试验以辅助医生进行诊断治疗。随着患病率的上升,HRM图像的数据量爆炸式增长,加之EMD的诊断流程较为复杂,临床上EMD误诊事件时有发生。为了提高EMD诊断的准确性,希望搭建一个计算机辅助诊断(Computer Aided Diagnosis,CAD)系统帮助医生对HRM图像进行自动分析。由于食管收缩活力的异常是诊断EMD的重要依据,该文提出了一个深度学习模型(PoS-ClasNet)以完成对HRM图像的食管收缩活力分类任务,为今后机器代替人工诊断EMD奠定基础。PoS-ClasNet作为一个多任务卷积神经网络(CNN)由PoSNet和S-ClasNet构成。前者用于HRM图像中吞咽框的检测和提取任务,后者根据食管吞咽特征鉴别收缩活力类型。实验使用了4000幅专家标记的HRM图像,用于训练、验证和测试的图像分别占比为70%,20%和10%。在测试集上,食管收缩活力分类器PoS-ClasNet的分类准确率高达93.25%,精度和召回率分别为93.39%和93.60%。结果表明PoS-ClasNet能较好地适应HRM图像数据的特性,在智能诊断食管收缩活力的任务中表现出了不俗的准确性和稳健性。将它应用在临床上辅助医生诊疗,会带来巨大的社会效益。

As the gold standard for the detection of Esophageal Motility Disorder(EMD),High-Resolution Manometry(HRM)is widely used in clinical tests to assist doctors in diagnosis.The amount of HRM images explodes with an increase in the prevalence rate,and the diagnostic process of EMD is complicated,both of which may lead to misdiagnosis of EMD in clinic.To improve the accuracy of the diagnosis of EMD,we hope to build a Computer Aided Diagnosis(CAD)system to assist doctors in analyzing HRM images automatically.Since the abnormality of esophageal contraction vigor is an important basis for diagnosis of EMD,in this paper,a Deep Learning(DL)model(PoS-ClasNet)is proposed to classify esophageal contraction vigor,which lays the foundation for machine to diagnose EMD instead of manual in the future.PoS-ClasNet,as a multi-task Convolutional Neural Network(CNN),is formed by PoSNet and S-ClassNet.The former is used to detect and extract swallowing frames in HRM images,while the latter identifies the type of contraction vigor based on esophageal swallowing characteristics.4,000 expert-labeled HRM images are used for the experiment,among which the images of training set,verification set and test set accounted for 70%,20%and 10%.On the test set,the classification accuracy of esophageal contraction vigor classifier PoS-ClasNet is as high as 93.25%,meanwhile the precision rate and the recall rate are 93.39%and 93.60%respectively.The experimental results show PoS-ClasNet can well adapt to the features of HRM image,with the outstanding accuracy and robustness in the task of intelligent diagnosis of esophageal contraction vigor.If the proposed model is used to assist doctors in clinical prevention,diagnosis and treatment,it will bring enormous social benefits.