Fully Automated Segmentation of the Psoas Major Muscle in Clinical CT Scans

  • Marcin KopaczkaEmail author
  • Richard Lindenpütz
  • Daniel Truhn
  • Maximilian Schulze-Hagen
  • Dorit Merhof
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


Clinical studies have shown that skeletal muscle mass, sarcopenia and muscle atrophy can be used as predictive indicators for morbidity and mortality after various surgical procedures and in different medical treatment methods. At the same time, the major psoas muscle has been has been used as a tool to assess total muscle volume. From the image processing side it has the advantage of being one of the few muscles that are not surrounded by other muscles at all times, thereby allowing simpler segmentation than in other muscles. The muscle is fully visible on abdominal CT scans, which are for example performed in clinical workups before surgery. Therefore, automatic analysis of the psoas major muscle in routine CT scans would aid in the assessment of sarcopenia without the need for additional scans or examinations. To this end, we present a method for fully automated segmentation of the psoas major muscle in abdominal CT scans using a combination of methods for semantic segmentation and shape analysis. Our method outperforms available approaches for this task, additionally we show a good correlation between muscle volume and population parameters in different clinical datasets.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kamiya N, Zhou X, Chen H, et al. Automated segmentation of psoas major muscle in x-ray CT images by use of a shape model: preliminary study. Radiological physics and technology. 2012;5(1):5–14.Google Scholar
  2. 2.
    Inoue T, Kitamura Y, Li Y, et al. Psoas major muscle segmentation using higher-order shape prior. In: International MICCAI Workshop on Medical Computer Vision. Springer; 2015. p. 116–124.Google Scholar
  3. 3.
    Hu P, Huo Y, Kong D, et al. Automated characterization of body composition and frailty with clinically acquired CT. In: International Workshop and Challenge on Computational Methods and Clinical Applications in Musculoskeletal Imaging. Springer; 2017. p. 25–35.Google Scholar
  4. 4.
    Heinrich MP, Blendowski M. Multi-organ segmentation using vantage point forests and binary context features. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2016. p. 598–606.Google Scholar
  5. 5.
    Meesters S, Yokota F, Okada T, et al. Multi atlas-based muscle segmentation in abdominal CT images with varying field of view; 2012. .Google Scholar
  6. 6.
    Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. p. 234–241.Google Scholar
  7. 7.
    Ҫiҫek Ö, Abdulkadir A, Lienkamp SS, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2016. p. 424–432.Google Scholar
  8. 8.
    Jégou S, Drozdzal M, Vazquez D, et al. The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2017. p. 11–19.Google Scholar
  9. 9.
    Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 3431–3440.Google Scholar
  10. 10.
    Antonakos E, Alabort-i Medina J, Tzimiropoulos G, et al. Feature-based Lucas–Kanade and active appearance models. IEEE Transactions on Image Processing. 2015;24(9):2617–2632.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Marcin Kopaczka
    • 1
    Email author
  • Richard Lindenpütz
    • 1
  • Daniel Truhn
    • 1
    • 2
  • Maximilian Schulze-Hagen
    • 2
  • Dorit Merhof
    • 1
  1. 1.Institute of Imaging and Computer VisionRWTH Aachen UniversityAachenDeutschland
  2. 2.Klinik für Diagnostische und Interventionelle RadiologieUniklinik AachenAachenDeutschland

Personalised recommendations