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Purpose: We propose a 2.5D deep learning neural network (DLNN) toautomatically classify thigh muscle into 11 classes and evaluate itsclassification accuracy over 2D and 3D DLNN when trained with limited datasets.Enables operator invariant quantitative assessment of the thigh muscle volumechange with respect to the disease progression. Materials and methods:Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOMimages. Cropped volumes were aligned with femur axis and resample in 2 mmvoxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained withaxial, coronal and sagittal muscle slices respectively. A voting algorithm wasused to combine the output of U-Nets to create final segmentation. 2.5D U-Netwas trained on PC with 38 TV and the remaining 10 TV were used to evaluatesegmentation accuracy of 10 classes within Thigh. The result segmentation ofboth 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-Netas 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 meanDSC score of 3D U-Net was 5.7% lower than that of 2.5D U-Net when trained withsame datasets. Conclusion: We achieved a faster computationally efficient andautomatic segmentation of thigh muscle into 11 classes with reasonableaccuracy. Enables quantitative evaluation of muscle atrophy with diseaseprogression.

Semantic Segmentation of Thigh Muscle using 2.5D Deep Learning Network Trained with Limited Datasets