Deep learning for abdominal adipose tissue segmentation with few labelled samples.

Journal: International journal of computer assisted radiology and surgery
PMID: 34845590

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.

Authors

  • Zheng Wang
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Alphonse Houssou Hounye
    School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
  • Jianglin Zhang
    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.
  • Muzhou Hou
    School of Mathematics and Statistics, Central South University, Changsha, 410083, China. houmuzhou@sina.com.
  • Min Qi
    Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage, Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.

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