Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning
Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning
复制标题
基于机器学习的磁共振脂肪分数图像全身脂肪组织自动分割
DOI：
10.1007/s10334-021-00958-5
复制
发表时间：
2021-09
影响因子：
2.3
通讯作者：
Chao Zou
中科院分区：
文献类型：
--
作者：
Zhiming Wang;Chuanli Cheng;Hao Peng;Yulong Qi;Qian Wan;Hongyu Zhou;Shaocheng Qu;Dong Liang;Xin Liu;Hairong Zheng;Chao Zou
ObjectiveTo propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images.Materials and methodsAdipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat–water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images).ResultsThe automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets.ConclusionThe proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.
登录
查看更多内容
影响因子：
4.2
作者：
S. Messiah;D. Vidot;Gabriel A. Somarriba;Kanathy Haney;S. Aytur;Ruby A. Natale;J. Brosco;K. Arheart-K.-Arhear
通讯作者：
S. Messiah;D. Vidot;Gabriel A. Somarriba;Kanathy Haney;S. Aytur;Ruby A. Natale;J. Brosco;K. Arheart-K.-Arhear
影响因子：
4.4
作者：
Kullberg, Joel;Johansson, Lars;Boernert, Peter
通讯作者：
Boernert, Peter
影响因子：
37.8
作者：
Abraham TM;Pedley A;Massaro JM;Hoffmann U;Fox CS
通讯作者：
Fox CS
影响因子：
4.2
作者：
L. Poll;H. Wittsack;J. Koch;R. Willers;A. Scherer;C. Kapitza;L. Heinemann;U. Mödder
通讯作者：
L. Poll;H. Wittsack;J. Koch;R. Willers;A. Scherer;C. Kapitza;L. Heinemann;U. Mödder
影响因子：
8.1
作者：
Shorten, Connor;Khoshgoftaar, Taghi M.
通讯作者：
Khoshgoftaar, Taghi M.