  Objective: To demonstrate the effectiveness of using a deep learning-based
approach for a fully automated slice-based measurement of muscle mass for
assessing sarcopenia on CT scans of the abdomen without any case exclusion
criteria.
  Materials and Methods: This retrospective study was conducted using a
collection of public and privately available CT images (n = 1070). The method
consisted of two stages: slice detection from a CT volume and single-slice CT
segmentation. Both stages used Fully Convolutional Neural Networks (FCNN) and
were based on a UNet-like architecture. Input data consisted of CT volumes with
a variety of fields of view. The output consisted of a segmented muscle mass on
a CT slice at the level of L3 vertebra. The muscle mass is segmented into
erector spinae, psoas, and rectus abdominus muscle groups. The output was
tested against manual ground-truth segmentation by an expert annotator.
  Results: 3-fold cross validation was used to evaluate the proposed method.
The slice detection cross validation error was 1.41+-5.02 (in slices). The
segmentation cross validation Dice overlaps were 0.97+-0.02, 0.95+-0.04,
0.94+-0.04 for erector spinae, psoas, and rectus abdominus, respectively, and
0.96+-0.02 for the combined muscle mass.
  Conclusion: A deep learning approach to detect CT slices and segment muscle
mass to perform slice-based analysis of sarcopenia is an effective and
promising approach. The use of FCNN to accurately and efficiently detect a
slice in CT volumes with a variety of fields of view, occlusions, and slice
thicknesses was demonstrated.
