Review of computer-aided face diagnosis for obstructive sleep apnea in children

doi:10.11772/j.issn.1001-9081.2020121963

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3394-3401.DOI: 10.11772/j.issn.1001-9081.2020121963

• Frontier and comprehensive applications • Previous Articles Next Articles

Review of computer-aided face diagnosis for obstructive sleep apnea in children

Jin ZHAO¹, Wen’ai SONG¹, Jun TAI²(), Jijiang YANG³, Qing WANG³, Xiaodan LI⁴, Yi LEI³, Yue QIU⁴

^1.School of Software，North University of China，Taiyuan Shanxi 030051，China
^2.Department of Otolaryngology-Head and Neck Surgery，Children’s Hospital Affiliated to Capital Institute of Pediatrics，Beijing 100020，China
^3.Department of Automation，Tsinghua University，Beijing 100084，China
^4.Department of Otolaryngology-Head and Neck Surgery，Beijing Children’s Hospital，Capital Medical University，Beijing 100045，China

Received:2020-12-14 Revised:2021-06-02 Accepted:2021-06-29 Online:2021-03-05 Published:2021-11-10
Contact: Jun TAI
About author:ZHAO Jin，born in 1995，M. S. candidate. His research interests include software engineering，digital medicine
SONG Wen’ai，born in 1964，Ph. D.，professor. Her research interests include digital medicine，computer vision
TAI Jun，born in 1978，Ph. D. candidate，chief physician. His research interests include children sleep disordered breathing disease
YANG Jijiang，born in 1968，Ph. D.，professor. His research interests include digital medicine
WANG Qing，born in 1977，Ph. D.，research fellow. His research interests include artificial intelligence aided diagnosis
LI Xiaodan， born in 1989， Ph. D.， resident physician. Her research interests include children sleep disordered breathing disease
LEI Yi，born in 1990，Ph. D.，assistant research fellow. His research interests include artificial intelligence aided diagnosis
QIU Yue，born in 1996，M. S.，resident physician. Her research interests include children sleep disordered breathing disease.
Supported by:
the Collaborative Innovation Project of Beijing Chaoyang District(CYXC2010)

儿童阻塞性睡眠呼吸暂停计算机人脸辅助诊断综述

赵津¹, 宋文爱¹, 邰隽²(), 杨吉江³, 王青³, 李晓丹⁴, 雷毅³, 邱悦⁴

^1.中北大学软件学院，太原 030051
^2.首都儿科研究所附属儿童医院耳鼻咽喉头颈外科，北京 100020
^3.清华大学自动化系，北京 100084
^4.首都医科大学附属北京儿童医院耳鼻咽喉头颈外科北京 100045

通讯作者: 邰隽
作者简介:赵津（1995—），男，山西阳泉人，硕士研究生，主要研究方向：软件工程、数字医疗
宋文爱（1964—），女，山西临汾人，教授，博士，主要研究方向：数字医疗、计算机视觉
邰隽（1978—），男，辽宁辽阳人，主任医师，博士研究生，主要研究方向：儿童睡眠呼吸障碍疾病
杨吉江（1968—），男，北京人，教授，博士，主要研究方向：数字医疗
王青（1977—），男，北京人，博士，研究员，主要研究方向：人工智能辅助诊断
李晓丹（1989—），女，山东莱芜人，住院医师，博士，主要研究方向：儿童睡眠呼吸障碍疾病
雷毅（1990—），山西汾阳人，男，助理研究员，博士，主要研究方向：人工智能辅助诊断
邱悦（1996—），女，江西省安人，住院医师，硕士，主要研究方向：儿童睡眠呼吸障碍疾病。
基金资助:
北京市朝阳区协同创新项目(CYXC2010)

Abstract

Abstract:

Using face images in the diagnosis of Obstructive Sleep Apnea （OSA） in children can reduce the burden of doctors and improve the accuracy of diagnosis. Firstly， the current methods and their limitations of OSA in children clinical diagnosis were briefly described. Then， on the basis of studying the existing methods， combining with the methods of computer-aided face diagnosis of other diseases， the computer-aided face diagnosis methods of OSA in children were divided into three types： traditional computer-aided face diagnosis methods， transfer learning based diagnosis methods， and 3D face data based diagnosis methods. The key steps of the three types of methods were summarized， and the methods used in these key steps were compared， which provides different entry points for the future research of computer-aided face diagnosis for OSA in children. Finally， the opportunities and challenges in the future research of OSA in children diagnosis were analyzed.

Key words: computer-aided diagnosis, Obstructive Sleep Apnea (OSA) in children, face key point detection, face feature extraction, transfer learning

摘要：

利用人脸图片辅助诊断儿童阻塞性睡眠呼吸暂停（OSA）可以减轻医生的负担，提高诊断的准确率。首先，简要阐述了目前儿童OSA临床诊断中的方法及其局限性；然后，在研究了目前已有方法的基础上，结合计算机人脸辅助诊断其他疾病的方法，将计算机人脸辅助诊断儿童OSA的方法分为三种类型：传统的计算机人脸辅助诊断方法、基于迁移学习的诊断方法、基于3D人脸数据的诊断方法，综述了三种类型的方法中的关键步骤，并对这些关键步骤中使用的方法进行了对比研究，为将来儿童OSA计算机人脸辅助诊断的研究提供了不同的切入点；最后，分析了儿童OSA诊断未来研究中的机遇和挑战。

关键词: 计算机辅助诊断, 儿童阻塞性睡眠呼吸暂停, 人脸关键点检测, 人脸特征提取, 迁移学习

CLC Number:

TP311

Jin ZHAO, Wen’ai SONG, Jun TAI, Jijiang YANG, Qing WANG, Xiaodan LI, Yi LEI, Yue QIU. Review of computer-aided face diagnosis for obstructive sleep apnea in children[J]. Journal of Computer Applications, 2021, 41(11): 3394-3401.

赵津, 宋文爱, 邰隽, 杨吉江, 王青, 李晓丹, 雷毅, 邱悦. 儿童阻塞性睡眠呼吸暂停计算机人脸辅助诊断综述[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3394-3401.

Figures/Tables 11

Fig. 1 Flow chart of traditional face aided diagnosis of OSA in children

Fig. 2 Flow chart of OAS diagnosis in children based on transfer learning

Fig. 3 Flow chart of OAS in childrendiagnosis based on 3D face data

Tab. 1 Comparison of advantages and disadvantages of face detection methods

方法	优点	缺点
基于特征的方法	简单，易于实现，对于光照、姿态的鲁棒性较高	某些特征由相关人员观察得出，局限性较大
基于AdaBoost方法	速度快，准确率较高	容易过拟合，Haar特征稳定性低
基于图像的方法	准确率高	需要更多的数据库，而且对于遮挡和光照的鲁棒性不高，容易受噪声影响

Fig. 4 Face keypoint detection

Tab. 2 Comparison of advantages and disadvantages of face keypoint detection methods

方法	优点	缺点
基于ASM传统方法	模型简单，易于理解，基本满足要求	运算效率低
基于级联回归方法	模型简单，实用性强，适合大规模数据	对于姿态较大的人脸效果很差
基于深度学习的方法	对于姿势变化较大和有遮挡的人脸识别精度也很高	普遍存在算法复杂，模型复杂度太高

Tab. 3 Comparison of advantages and disadvantages of face feature extraction methods

方法	优点	缺点	改进方法
基于欧氏距离的几何特征提取	方便实用	从3D投影到2D时，脸上的关键点实际距离差别很大	用2D人脸坐标进行3D人脸重构等
基于Gabor小波变换的纹理特征提取	简单实用，可以提取到空间局部的低频特征	提取到的特征信息不全面，维度高，数据庞大	降维PCA；二维Gabor小波与AR-LGC 相结合的人脸特征提取等
基于颜色直方图的颜色特征提取	反映了图像中颜色的分布，可以比较图像间的颜色差	丢失了像素点间的位置特征	颜色集等
基于神经网络的深度特征提取	准确度高	结构复杂，需要大规模的数据库进行训练	不断改进模型的参数和优化方法，建立更多姿态的2D人脸数据库等

Tab. 4 Transfer learning network models used in medical field and corresponding source domain data sets

方案	源域数据集	网络模型
文献［33］方案	ImageNet	OverFeat
文献［42］方案	ImageNet	VGGNet-16
文献［35］方案	ImageNet	GoogleNet Inception v3
文献［34］方案	ImageNet	VGGNet-16和ResNet-50
文献［36］方案	CASIA Web-Face	深格塔式的深卷积神经网络结构
文献［17］方案	CASIA Web-Face	10个卷积层的DCNN，并把大内核分解为多个较小内核的多层网络
文献［37］方案	VGG-Face	5个卷积层的DCNN

Tab. 5 Characteristics comparison of common 3D face data acquisition methods

3D人脸模型	方法	特点
CT	用X射线生成人体横截面	包含有关软组织和硬组织的3D信息，费用昂贵，病人暴露在辐射下
深度图	将3D面的 $z$ 轴值投影到2D平面上	扫描速度快，精确度较高，但不适用于3D视频录制
点云	测量光图案的变形以计算表面形状	扫描速度更快，但精确度与深度图相比有所下降
网格	匹配不同摄像机采集到的点，并计算三维位置	包含的三维信息更多，模型更精确，但需要大量的内存
3dMD系统	六对同步摄像头组成，用三角剖分合并图像	无辐射影响，精确度高

Tab. 5 Characteristics comparison of common 3D face data acquisition methods

3D人脸模型	方法	特点
CT	用X射线生成人体横截面	包含有关软组织和硬组织的3D信息，费用昂贵，病人暴露在辐射下
深度图	将3D面的 $z$ 轴值投影到2D平面上	扫描速度快，精确度较高，但不适用于3D视频录制
点云	测量光图案的变形以计算表面形状	扫描速度更快，但精确度与深度图相比有所下降
网格	匹配不同摄像机采集到的点，并计算三维位置	包含的三维信息更多，模型更精确，但需要大量的内存
3dMD系统	六对同步摄像头组成，用三角剖分合并图像	无辐射影响，精确度高

Fig. 5 Three commonly used 3D face structures

Tab. 6 Characteristics comparison of 3D face data feature extraction methods

方案	方法	特点
文献［51-53］方案	使用线性距离和角度	简单实用，易于测量和比较，但在形状分析上会有很多困难
文献［13］方案	持久同源性	持久同源性结合了几何的区分能力和拓扑的分类能力
文献［14］方案	GMM	对整体形状进行分析，还可以减少误差
文献［13］方案	使用神经网络自动提取深度表型特征	神经网络可以学到更深层次的深度表型特征，理论上可以更好地进行分类，但是需要庞大的数据库、参数、优化方法

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Review of computer-aided face diagnosis for obstructive sleep apnea in children

儿童阻塞性睡眠呼吸暂停计算机人脸辅助诊断综述

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