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Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

To 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 methods

Adipose 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).

Results

The 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.

Conclusion

The 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.

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Funding

This research was supported by the National natural Science Foundation of China [Grant Number 61901462 and 81801724], the China Postdoctoral Science Foundation [Grant Number 2019M650220], the Guangdong Grant ‘Key Technologies for Treatment of Brain Disorders’ [Grant Number 2018B030332001], Scientific Instrument Innovation Team of the Chinese Academy of Sciences [Grant Number GJJSTD20180002], International Partnership Program of Chinese Academy of Sciences Grant [Grant Number 154144KYSB20180063], Shenzhen Double Chain Grant [Grant Number [2018]256], and the Strategic Priority Research Program of Chinese Academy of Sciences [Grant Number XDB25000000].

Author information

Author notes

  1. Zhiming Wang and Chuanli Cheng have contributed equally to this work.

Authors and Affiliations

  1. College of Physical Science and Technology, Central China Normal University, Wuhan, China

    Zhiming Wang & Shaocheng Qu

  2. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhiming Wang, Chuanli Cheng, Hao Peng, Qian Wan, Hongyu Zhou, Dong Liang, Xin Liu, Hairong Zheng & Chao Zou

  3. University of Chinese Academy of Sciences, Beijing, China

    Chuanli Cheng, Dong Liang, Xin Liu, Hairong Zheng & Chao Zou

  4. Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, China

    Chuanli Cheng, Dong Liang, Xin Liu, Hairong Zheng & Chao Zou

  5. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

    Hao Peng

  6. Radiology Department, Peking University Shenzhen Hospital, Shenzhen, China

    Yulong Qi

Authors
  1. Zhiming Wang
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  2. Chuanli Cheng
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  3. Hao Peng
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  8. Dong Liang
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  9. Xin Liu
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  11. Chao Zou
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Contributions

ZW and CC have made substantial contributions to the conception and design of the study, to the acquisition, analysis, and interpretation of data, and have drafted the manuscript. CZ and SQ have made substantial contributions to the conception and design of the work, to the acquisition, analysis, and interpretation of data, and have substantially revised the manuscript. HP, YQ, QW, and HZ have made substantial contributions to the acquisition, analysis, and interpretation of data. DL, XL, and HrZ have made substantial contributions to the conception and design of the work and substantively have revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chao Zou.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (IRB approval number: SIAT-IRB-200315-H0454).

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Wang, Z., Cheng, C., Peng, H. et al. Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning. Magn Reson Mater Phy 35, 193–203 (2022). https://doi.org/10.1007/s10334-021-00958-5

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  • DOI: https://doi.org/10.1007/s10334-021-00958-5

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