Three-dimensional numerical schemes for the segmentation of the psoas
  muscle in X-ray computed tomography images
Giulio Paolucci Isabella Cama Cristina Campi Michele Piana
The analysis of the psoas muscle in morphological and functional imaging has
proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of
skeletal muscle mass and function that may be correlated to multifactorial
etiological aspects. The inclusion of sarcopenia assessment into a radiological
workflow would need the implementation of computational pipelines for image
processing that guarantee segmentation reliability and a significant degree of
automation. The present study utilizes three-dimensional numerical schemes for
psoas segmentation in low-dose X-ray computed tomography images. Specifically,
here we focused on the level set methodology and compared the performances of
two standard approaches, a classical evolution model and a three-dimension
geodesic model, with the performances of an original first-order modification
of this latter one. The results of this analysis show that these gradient-based
schemes guarantee reliability with respect to manual segmentation and that the
first-order scheme requires a computational burden that is significantly
smaller than the one needed by the second-order approach.