Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging.

Bingxia WuChuanli ChengYulong QiHongyu ZhouHao PengQian WanXin LiuHairong ZhengHuimao ZhangChao Zou

Published in: Magma (New York, N.Y.) (2022)

The present study demonstrates that the proposed automatic segmentation method using U-Net network is able to identify human sclavAT efficiently and accurately.

Keyphrases

deep learning
endothelial cells
magnetic resonance imaging
adipose tissue
high resolution
convolutional neural network
machine learning
magnetic resonance
computed tomography
contrast enhanced
insulin resistance
type diabetes
mass spectrometry
metabolic syndrome
high speed