Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images

Abstract

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.

Keywords: Image segmentation; Sarcopenia; Three-dimensional level set methods; X-ray Computed Tomography (CT).

Algorithm 1 Cleaning procedure

Fig. 1

Coronal view of the psoas muscles (in yellow) and the corresponding axial views at different levels of the abdomen

Fig. 2

Segmentations provided by the classical evolution model (2) (‘classical’, first column), the first-order geodesic model (5) (‘GMFD 1ord’, second column), and the second order geodesic model (4) (‘GMFD 2ord’, third column). First row: subject CTTO02 (45 slices) with $p=2$ and $\sigma =0$. Second row: subject PZGE01 (33 slices) with $p=2$ and $\sigma =2$. Third row: subject PZGE02 (35 slices) with $p=2$ and $\sigma =2$. Fourth row: subject PZGE03 (31 slices) with $p=2$ and $\sigma =2$. Fifth row: subject PZTO03 (52 slices) with $p=2$ and $\sigma =0$. Sixth row: subject PZTO04 (51 slices) with $p=2$ and $\sigma =1$

Fig. 3

Post-processing pipeline for the patients CTTO01, CTTO03, and PZTO01 when applied to the outcomes of ‘GMFD 1ord’. First column: the starting data. Second colum: the outcome of the first step (starting slice: 20 for CTTO01, 44 for CTTO03, 25 for PZTO01). Third column: the outcome of the second step

Fig. 4

Different segmentations for one of the six subjects in the CMB-PCA dataset. First row, from left to right: ground truth, TS-FM and TS-FRM segmentations. Second row, from left to right: ‘classical’, ‘GMFD 1ord’ and ‘GMFD 2ord’ segmentations