Automated Assessment of Sarcopenia with Hounsfield Unit Average Calculation in Computed Tomography Scans Using Deep Learning Techniques

Abstract Introduction Skeletal muscle is increasingly plastic with an ability to gain or lose tissue. Depletion of muscle mass and quality occurs due to various factors such as aging, disease, and disuse. Sarcopenia can be loosely defined as a significant loss of muscle mass and function. Sarcopenia is now recognized as an independent risk factor for various patient-related negative outcomes after various surgeries. Various computed tomography (CT) based imaging indices for assessment of sarcopenia exist in practice. The psoas muscle Hounsfield unit average calculation (HUAC) has been proven to be an effective one as it is independent of patient anthropometric data, and it can be calculated in the images provided. Aim The aim of this study is to develop automated tools for estimation of the HUAC using deep learning algorithms. Materials and Methods A total of 41 abdominal CTs were used. Ground truth was established and validated by two radiologists with more than 5 and 10 years of experience each. Models were trained to identify the psoas muscle among the slices and calculate the HUAC. Results At inference, an average intersection over union (IoU) value of 90% was obtained between the deep learning model outputs and the original annotated test images for the CT slices. The Dice coefficient was 0.90 between the ground truth labels and the output from the model. Conclusion We have demonstrated the accuracy of our deep learning–based algorithm for quantifying the psoas muscle HUAC, which is a marker for sarcopenia. There is a potential for a fully automated measure to calculate the HUAC for any patient undergoing CT scan.