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Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images

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Abstract

Objective

The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net model.

Materials and methods

Thirty-four rats fed different diets or housed at different temperatures underwent successive MR scans before and after NE injection. The iBAT were labelled automatically by identifying the regions with obvious FF change in response to the NE stimulation. Further, these FF images along with the recognized iBAT mask images were used to develop a deep learning network to accomplish the robust segmentation of iBAT in various rat models, even without NE stimulation. The trained model was then validated in rats fed with high-fat diet (HFD) in comparison with normal diet (ND).

Result

A total of 6510 FF images were collected using a clinical 3.0 T MR scanner. The dice similarity coefficient (DSC) between the automatic and manual labelled results was 0.895 ± 0.022. For the network training, the DSC, precision rate, and recall rate were found to be 0.897 ± 0.061, 0.901 ± 0.068 and 0.899 ± 0.086, respectively. The volumes and FF values of iBAT in HFD rats were higher than ND rats, while the FF decrease was larger in ND rats after NE injection.

Conclusion

An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.

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Availability of data and materials

The datasets used in the current study are available from the corresponding author on reasonable request.

Abbreviations

iBAT:

Interscapular brown adipose tissue

WAT:

White adipose tissue

FF:

Fat fraction

NE:

Noradrenaline

HFD:

High-fat diet

ND:

Normal diet

SD:

Sprague Dawley

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Funding

This research was supported by the National key research and development program of China: 2023YFC2410504, Natural Science Foundation of Guangdong Province: 2022A1515010162, Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province: 2023B1212060052, Scientific Instrument Innovation Team of the Chinese Academy of Sciences: GJJSTD20180002.

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Authors and Affiliations

  1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China

    Chuanli Cheng, Qian Wan, Hao Peng, Xin Liu, Hairong Zheng & Chao Zou

  2. Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China

    Chuanli Cheng & Chao Zou

  3. School of Information Engineering, Wuhan University of Technology, Wuhan, China

    Bingxia Wu

  4. Radiology Department, Bethune First Hospital of Jilin University, Changchun, China

    Lei Zhang & Huimao Zhang

Authors
  1. Chuanli Cheng
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  9. Chao Zou
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Contributions

CC: conception, design, provision of study materials and writing. BW: provision of study materials, data acquisition and writing. LZ: data analysis and interpretation, and writing—editing. QW: data acquisition and writing—editing. HP: data acquisition and writing—editing. XL: data analysis and interpretation, and writing—editing. HZ: administrative support and writing—editing. HZ: data analysis and interpretation, and writing-editing. CZ: conception, design and manuscript writing—review and editing.

Corresponding author

Correspondence to Chao Zou.

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All authors declare that they have no conflict of interest.

Ethical approval

The animals study received care in accordance with the Guidance Suggestions for the Care and Use of Laboratory Animals and were used under protocols approved by Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Animal Care and Use Committee. (IRB approval number: SIAT-IACUC-190218-YGS-CCL-A0624).

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Cheng, C., Wu, B., Zhang, L. et al. Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images. Magn Reson Mater Phy 37, 215–226 (2024). https://doi.org/10.1007/s10334-023-01133-8

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