Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images
This work addresses sarcopenia assessment in radiology by improving automation and efficiency in muscle segmentation, though it is incremental as it modifies existing methods.
The study tackled the problem of segmenting the psoas muscle in low-dose X-ray CT images to assess sarcopenia, finding that a first-order gradient-based scheme achieved reliability comparable to manual segmentation while significantly reducing computational burden compared to a second-order approach.
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.