Automatic Segmentation of Abdominal Subcutaneous Adipose Layer in Ultrasound Image Using CNN

摘要：

　　Accurate measurement of the thickness of subcutaneous adipose tissue(SAT)can effectively estimate body composition.Ultrasound is an accurate technique for measuring the thickness of SAT layer.However,at different body sites,the SAT layer has different content of inlaid fibrous structure which causes segmentation of the SAT layer to be very difficult.This paper presents a fully automatic approach to detect and extract the border of the SAT layer using Convolutional Neural Network(CNN)techniques.Our approach utilizes CNN to learn the complex regression function that maps the borders into their positions in images.The SAT layer is segmented according to the predicted upper and lower borders and its thickness is calculated automatically.The average predicted error on simulated ultrasound images achieves 0.34mm or 1.88%of the SAT thickness.On tested human abdominal ultrasound images,we obtain the average prediction error of 0.85mm or 8.71%of the SAT thickness.This study is suitable for segmenting subcutaneous fat tissue,and it may be used as a general framework for applications of regression CNN to identify other specific borders on ultrasound images.

关键词： SAT CNN Regression Segmentation Fully automatic

作者: Ya-lan TAN Paul LIU Hao YIN Dong C.LIU

作者单位: Sichuan University,College of Computer Science,No.24 South Section 1,Yihuan Road,Chengdu,China Stork Healthcare Co.,Ltd,Chuangye Road,Hi-Tech Zone,Chengdu,China

会议类型: 国际会议

会议名称: 2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

会议地点: 上海

会议语种:英文

页码: 258-264

在线出版日期: 2019-06-16（万方平台首次上网日期，不代表论文的发表时间）