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Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

  • Peijun HuEmail author
  • Yuankai Huo
  • Dexing Kong
  • J. Jeffrey Carr
  • Richard G. Abramson
  • Katherine G. Hartley
  • Bennett A. Landman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10734)

Abstract

Quantification of fat and muscle on clinically acquired computed tomography (CT) scans is critical for determination of body composition, a key component of health. Manual tracing has been regarded as the gold standard method of body segmentation; however, manual tracing is time-consuming. Many semi-automated/automated algorithms have been proposed to avoid the manual efforts. Previous efforts largely focused on segmenting two-dimensional cross-sectional images (e.g., at L3/T4 vertebra locations) rather than on the whole-body volume. In this paper, we propose a fully automated three-dimensional (3D) body composition estimation framework for segmenting the muscle and fat from abdominal CT scans. The 3D whole body segmentations are reconstructed from a slice-wise multi-atlas label fusion (MALF) based framework. First, we use a low-dimensional atlas representation to estimate each class for each axial slice. Second, the abdominal wall and psoas muscle are segmented by combining MALF with active shape models and deformable models. Third, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are measured to assess the areas of muscle and fat tissue. The proposed method was compared to manual segmentation and demonstrated high accuracy. Then, we evaluated the approach on 40 CT scans comparing the new method to a prior atlas-based segmentation method and achieved 0.854, 0.740, 0.887 and 0.933 on Dice similarity index for the skeletal muscle, psoas muscle, VAT and SAT, respectively. Compared with the baseline, our method showed significantly (\(p\,{<}\,0.001\)) higher accuracy on skeletal muscle, VAT and SAT estimation.

Keywords

Skeletal muscle Psoas muscle Visceral fat Subcutaneous fat Multi-atlas 

Notes

Acknowledgements

This research was supported by NIH 1R03EB012461, NIH 2R01 EB006136, NIH R01EB006193, NIH P30 CA068485, NIH R01 HL 098445 (PI - Carr), and AUR GE Radiology Research Academic Fellowship, and in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN. This project was supported in part by VISE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This research was supported in part by the National Natural Science Foundation of China (Grant No. 91630311) and the Fundamental Research Funds for the Central Universities (Grant No. 2017XZZX007-02).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Peijun Hu
    • 1
    • 2
    Email author
  • Yuankai Huo
    • 3
  • Dexing Kong
    • 1
  • J. Jeffrey Carr
    • 4
  • Richard G. Abramson
    • 4
  • Katherine G. Hartley
    • 4
  • Bennett A. Landman
    • 2
    • 3
    • 4
  1. 1.School of Mathematical SciencesZhejiang UniversityHangzhouChina
  2. 2.Computer ScienceVanderbilt UniversityNashvilleUSA
  3. 3.Electrical EngineeringVanderbilt UniversityNashvilleUSA
  4. 4.Radiology and Radiological SciencesVanderbilt UniversityNashvilleUSA

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