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Deep learning for abdominal adipose tissue segmentation with few labelled samples

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Abstract

Purpose

Fully automated abdominal adipose tissue segmentation from computed tomography (CT) scans plays an important role in biomedical diagnoses and prognoses. However, to identify and segment subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in the abdominal region, the traditional routine process used in clinical practise is unattractive, expensive, time-consuming and leads to false segmentation. To address this challenge, this paper introduces and develops an effective global-anatomy-level convolutional neural network (ConvNet) automated segmentation of abdominal adipose tissue from CT scans termed EFNet to accommodate multistage semantic segmentation and high similarity intensity characteristics of the two classes (VAT and SAT) in the abdominal region.

Methods

EFNet consists of three pathways: (1) The first pathway is the max unpooling operator, which was used to reduce computational consumption. (2) The second pathway is concatenation, which was applied to recover the shape segmentation results. (3) The third pathway is anatomy pyramid pooling, which was adopted to obtain fine-grained features. The usable anatomical information was encoded in the output of EFNet and allowed for the control of the density of the fine-grained features.

Results

We formulated an end-to-end manner for the learning process of EFNet, where the representation features can be jointly learned through a mixed feature fusion layer. We immensely evaluated our model on different datasets and compared it to existing deep learning networks. Our proposed model called EFNet outperformed other state-of-the-art models on the segmentation results and demonstrated tremendous performances for abdominal adipose tissue segmentation.

Conclusion

EFNet is extremely fast with remarkable performance for fully automated segmentation of the VAT and SAT in abdominal adipose tissue from CT scans. The proposed method demonstrates a strength ability for automated detection and segmentation of abdominal adipose tissue in clinical practise.

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Funding

This work was supported by Scientific Research Fund of Hunan Provincial Education Department(grant number 20C0402) and by Hunan First Normal University(grant number XYS16N03), also funded by the Projects of the National Social Science Foundation of China (grant number 82073019 and 82073018) and Hunan National Applied Mathematics Centre (grant number 2020ZYT003).

Author information

Author notes

  1. Zheng Wang and Alphonse Houssou Hounye These authors contributed equally to this work.

Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, China

    Zheng Wang, Alphonse Houssou Hounye & Muzhou Hou

  2. Science and Engineering School, Hunan First Normal University, Changsha, 410205, China

    Zheng Wang

  3. Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China

    Min Qi

  4. Department of Detmatology, The Second Clinical Medical College, Shenzhen Peoples Hospital, Jinan University. The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China

    Jianglin Zhang

Authors
  1. Zheng Wang
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  4. Muzhou Hou
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  5. Min Qi
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Corresponding authors

Correspondence to Muzhou Hou or Min Qi.

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Wang, Z., Hounye, A.H., Zhang, J. et al. Deep learning for abdominal adipose tissue segmentation with few labelled samples. Int J CARS 17, 579–587 (2022). https://doi.org/10.1007/s11548-021-02533-8

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