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

Abstract

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.