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A Deep Learning Based Geographic Attention Model for Body Composition Tissue Segmentation.

Jian Dai1, Jayaram K Udupa2, Drew A Torigian2

  • 1School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei China 066004.

Proceedings of Spie--The International Society for Optical Engineering
|June 1, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces GA-Net, a deep learning model for precise body composition analysis from PET/CT scans. It improves segmentation accuracy, especially for difficult boundaries, and reduces data needs for training.

Keywords:
Body composition analysis (BCA)body composition segmentationbody tissue segmentationconvolutional neural networks (CNN)deep neural networksfully convolutional networks

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Computed tomography (CT) enables practical body composition analysis, crucial for clinical and research applications.
  • Deep learning has advanced automatic body composition analysis (BCA), but faces challenges with tissue boundary segmentation and large dataset requirements.
  • Accurate segmentation of adipose tissue, skeletal muscle, and skeleton is vital for understanding health and pathology.

Purpose of the Study:

  • To propose a novel deep learning approach, Geographic Attention Network (GA-Net), for enhanced body composition tissue segmentation on PET/CT images.
  • To leverage body area information to improve segmentation accuracy, particularly for tissues with indistinguishable boundaries.
  • To reduce the dependency on large-scale datasets for training deep neural networks in BCA.

Main Methods:

  • Development of the Geographic Attention Network (GA-Net), a deep learning model incorporating body area information for segmentation.
  • Application of GA-Net to segment four key tissues: subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle tissue (SMT), and skeleton (Sk).
  • Evaluation on a dataset of 50 body torso PET/CT scans, assessing performance with limited training data.

Main Results:

  • GA-Net achieved precise segmentation of multiple body composition tissues, outperforming existing methods, especially for challenging boundaries.
  • The inclusion of geographic information significantly enhanced the model's representation ability and segmentation accuracy.
  • The method demonstrated effectiveness in reducing data requirements for training deep learning networks for BCA.

Conclusions:

  • GA-Net offers a robust and accurate solution for body composition tissue segmentation on PET/CT images.
  • The approach effectively addresses limitations in current deep learning-based BCA methods, particularly concerning segmentation precision and data efficiency.
  • This method holds promise for advancing clinical and research applications of body composition analysis.