  Purpose: We propose a 2.5D deep learning neural network (DLNN) to
automatically classify thigh muscle into 11 classes and evaluate its
classification accuracy over 2D and 3D DLNN when trained with limited datasets.
Enables operator invariant quantitative assessment of the thigh muscle volume
change with respect to the disease progression. Materials and methods:
Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOM
images. Cropped volumes were aligned with femur axis and resample in 2 mm
voxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained with
axial, coronal and sagittal muscle slices respectively. A voting algorithm was
used to combine the output of U-Nets to create final segmentation. 2.5D U-Net
was trained on PC with 38 TV and the remaining 10 TV were used to evaluate
segmentation accuracy of 10 classes within Thigh. The result segmentation of
both left and right thigh were de-cropped to original CT volume space. Finally,
segmentation accuracies were compared between proposed DLNN and 2D/3D U-Net.
Results: Average segmentation DSC score accuracy of all classes with 2.5D U-Net
as 91.18% and Average Surface distance (ASD) accuracy as 0.84 mm. We found,
mean DSC score for 2D U-Net was 3.3% lower than the that of 2.5D U-Net and mean
DSC score of 3D U-Net was 5.7% lower than that of 2.5D U-Net when trained with
same datasets. Conclusion: We achieved a faster computationally efficient and
automatic segmentation of thigh muscle into 11 classes with reasonable
accuracy. Enables quantitative evaluation of muscle atrophy with disease
progression.
